{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/drug-discovery/papers/3","list_of":"/task/drug-discovery","task":"Drug Discovery","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":3,"pages_in_order":14,"rows_per_page":100,"rows":[201,300],"of":1337,"counts":{"archive_papers_tagged":1337,"with_a_code_link":566,"where_syntology_ran_a_sample":182,"not_listed_spam_title":0,"listed":1337,"listed_where_code_ran":182,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":146,"every_run_a_failure_of_syntologys_instrument":36,"listed_with_a_run_with_no_instrument_failure":146,"listed_every_run_a_failure_of_syntologys_instrument":36,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/drug-discovery","prev":"/task/drug-discovery/papers/2","next":"/task/drug-discovery/papers/4","papers":[{"url":"/paper/conditional-prediction-roc-bands-for-graph","slug":"conditional-prediction-roc-bands-for-graph","title":"Conditional Prediction ROC Bands for Graph Classification","date":"2024-10-20","arxiv_id":"2410.15239","repositories_listed":1,"syntology":null},{"url":"/paper/explaining-graph-neural-networks-with-large","slug":"explaining-graph-neural-networks-with-large","title":"Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective for Molecular Property Prediction","date":"2024-10-19","arxiv_id":"2410.15165","repositories_listed":1,"syntology":null},{"url":"/paper/flexmol-a-flexible-toolkit-for-benchmarking","slug":"flexmol-a-flexible-toolkit-for-benchmarking","title":"FlexMol: A Flexible Toolkit for Benchmarking Molecular Relational Learning","date":"2024-10-19","arxiv_id":"2410.15010","repositories_listed":1,"syntology":null},{"url":"/paper/fragnet-a-graph-neural-network-for-molecular","slug":"fragnet-a-graph-neural-network-for-molecular","title":"FragNet: A Graph Neural Network for Molecular Property Prediction with Four Levels of Interpretability","date":"2024-10-16","arxiv_id":"2410.12156","repositories_listed":1,"syntology":null},{"url":"/paper/large-scale-knowledge-integration-for","slug":"large-scale-knowledge-integration-for","title":"Large-Scale Knowledge Integration for Enhanced Molecular Property Prediction","date":"2024-10-15","arxiv_id":"2410.11914","repositories_listed":1,"syntology":null},{"url":"/paper/mf-lal-drug-compound-generation-using-multi","slug":"mf-lal-drug-compound-generation-using-multi","title":"MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning","date":"2024-10-15","arxiv_id":"2410.11226","repositories_listed":1,"syntology":{"n":9,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/mf-lal-drug-compound-generation-using-multi#ran","syntology_url":"https://syntology.ai/paper/2410.11226","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.11226"}},"official":{"repos":["rose-stl-lab/mf-lal"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/kindel-dna-encoded-library-dataset-for-kinase","slug":"kindel-dna-encoded-library-dataset-for-kinase","title":"KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors","date":"2024-10-11","arxiv_id":"2410.08938","repositories_listed":1,"syntology":null},{"url":"/paper/unveiling-molecular-secrets-an-llm-augmented","slug":"unveiling-molecular-secrets-an-llm-augmented","title":"Unveiling Molecular Secrets: An LLM-Augmented Linear Model for Explainable and Calibratable Molecular Property Prediction","date":"2024-10-11","arxiv_id":"2410.08829","repositories_listed":1,"syntology":null},{"url":"/paper/generative-artificial-intelligence-for-2","slug":"generative-artificial-intelligence-for-2","title":"Generative Artificial Intelligence for Navigating Synthesizable Chemical Space","date":"2024-10-04","arxiv_id":"2410.03494","repositories_listed":1,"syntology":null},{"url":"/paper/deepprotein-deep-learning-library-and","slug":"deepprotein-deep-learning-library-and","title":"DeepProtein: Deep Learning Library and Benchmark for Protein Sequence Learning","date":"2024-10-02","arxiv_id":"2410.02023","repositories_listed":1,"syntology":null},{"url":"/paper/analysis-of-gene-regulatory-networks-from","slug":"analysis-of-gene-regulatory-networks-from","title":"Analysis of Gene Regulatory Networks from Gene Expression Using Graph Neural Networks","date":"2024-09-20","arxiv_id":"2409.13664","repositories_listed":1,"syntology":null},{"url":"/paper/cradle-vae-enhancing-single-cell-gene","slug":"cradle-vae-enhancing-single-cell-gene","title":"CRADLE-VAE: Enhancing Single-Cell Gene Perturbation Modeling with Counterfactual Reasoning-based Artifact Disentanglement","date":"2024-09-09","arxiv_id":"2409.05484","repositories_listed":1,"syntology":null},{"url":"/paper/a-unified-approach-to-inferring-chemical","slug":"a-unified-approach-to-inferring-chemical","title":"A Unified Approach to Inferring Chemical Compounds with the Desired Aqueous Solubility","date":"2024-09-06","arxiv_id":"2409.04301","repositories_listed":1,"syntology":null},{"url":"/paper/enhancing-uncertainty-quantification-in-drug","slug":"enhancing-uncertainty-quantification-in-drug","title":"Enhancing Uncertainty Quantification in Drug Discovery with Censored Regression Labels","date":"2024-09-06","arxiv_id":"2409.04313","repositories_listed":1,"syntology":null},{"url":"/paper/data-driven-parametrization-of-molecular","slug":"data-driven-parametrization-of-molecular","title":"Data-Driven Parametrization of Molecular Mechanics Force Fields for Expansive Chemical Space Coverage","date":"2024-08-23","arxiv_id":"2408.12817","repositories_listed":1,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/data-driven-parametrization-of-molecular#ran","syntology_url":"https://syntology.ai/paper/2408.12817","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.12817"}},"official":{"repos":["bytedance/byteff"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/evaluation-framework-for-ai-driven-molecular","slug":"evaluation-framework-for-ai-driven-molecular","title":"Evaluation Framework for AI-driven Molecular Design of Multi-target Drugs: Brain Diseases as a Case Study","date":"2024-08-20","arxiv_id":"2408.10482","repositories_listed":1,"syntology":null},{"url":"/paper/molecular-graph-representation-learning","slug":"molecular-graph-representation-learning","title":"Molecular Graph Representation Learning Integrating Large Language Models with Domain-specific Small Models","date":"2024-08-19","arxiv_id":"2408.10124","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-latent-space-for-generating-peptide","slug":"exploring-latent-space-for-generating-peptide","title":"Exploring Latent Space for Generating Peptide Analogs Using Protein Language Models","date":"2024-08-15","arxiv_id":"2408.08341","repositories_listed":1,"syntology":null},{"url":"/paper/protein-language-models-and-machine-learning","slug":"protein-language-models-and-machine-learning","title":"Protein Language Models and Machine Learning Facilitate the Identification of Antimicrobial Peptides","date":"2024-08-14","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/integration-of-genetic-algorithms-and-deep","slug":"integration-of-genetic-algorithms-and-deep","title":"Integration of Genetic Algorithms and Deep Learning for the Generation and Bioactivity Prediction of Novel Tyrosine Kinase Inhibitors","date":"2024-08-13","arxiv_id":"2408.07155","repositories_listed":1,"syntology":null},{"url":"/paper/cell-morphology-guided-small-molecule","slug":"cell-morphology-guided-small-molecule","title":"Cell Morphology-Guided Small Molecule Generation with GFlowNets","date":"2024-08-09","arxiv_id":"2408.05196","repositories_listed":1,"syntology":{"n":13,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":10,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 10 unverified","sample_list":"/paper/cell-morphology-guided-small-molecule#ran","syntology_url":"https://syntology.ai/paper/2408.05196","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2408.05196"}},"official":{"repos":["thematrixmaster/omics-guided-gfn"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":10,"ran_from_kinds":["official"]}}},{"url":"/paper/achieving-well-informed-decision-making-in","slug":"achieving-well-informed-decision-making-in","title":"Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity Models","date":"2024-07-19","arxiv_id":"2407.14185","repositories_listed":1,"syntology":null},{"url":"/paper/context-guided-diffusion-for-out-of","slug":"context-guided-diffusion-for-out-of","title":"Context-Guided Diffusion for Out-of-Distribution Molecular and Protein Design","date":"2024-07-16","arxiv_id":"2407.11942","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/context-guided-diffusion-for-out-of#ran","syntology_url":"https://syntology.ai/paper/2407.11942","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.11942"}},"official":{"repos":["leojklarner/context-guided-diffusion"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/directly-optimizing-for-synthesizability-in","slug":"directly-optimizing-for-synthesizability-in","title":"Directly Optimizing for Synthesizability in Generative Molecular Design using Retrosynthesis Models","date":"2024-07-16","arxiv_id":"2407.12186","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/directly-optimizing-for-synthesizability-in#ran","syntology_url":"https://syntology.ai/paper/2407.12186","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.12186"}},"official":{"repos":["schwallergroup/saturn"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/molecule-language-model-with-augmented-pairs","slug":"molecule-language-model-with-augmented-pairs","title":"Vision Language Model is NOT All You Need: Augmentation Strategies for Molecule Language Models","date":"2024-07-12","arxiv_id":"2407.09043","repositories_listed":1,"syntology":null},{"url":"/paper/accelerating-drug-safety-assessment-using-2","slug":"accelerating-drug-safety-assessment-using-2","title":"Accelerating Drug Safety Assessment using Bidirectional-LSTM for SMILES Data","date":"2024-07-08","arxiv_id":"2407.18919","repositories_listed":1,"syntology":null},{"url":"/paper/benchmark-on-drug-target-interaction-modeling","slug":"benchmark-on-drug-target-interaction-modeling","title":"Benchmark on Drug Target Interaction Modeling from a Structure Perspective","date":"2024-07-04","arxiv_id":"2407.04055","repositories_listed":1,"syntology":null},{"url":"/paper/nebula-neural-empirical-bayes-under-latent","slug":"nebula-neural-empirical-bayes-under-latent","title":"NEBULA: Neural Empirical Bayes Under Latent Representations for Efficient and Controllable Design of Molecular Libraries","date":"2024-07-03","arxiv_id":"2407.03428","repositories_listed":1,"syntology":null},{"url":"/paper/yzs-model-a-predictive-model-for-organic-drug","slug":"yzs-model-a-predictive-model-for-organic-drug","title":"YZS-model: A Predictive Model for Organic Drug Solubility Based on Graph Convolutional Networks and Transformer-Attention","date":"2024-06-27","arxiv_id":"2406.19136","repositories_listed":1,"syntology":null},{"url":"/paper/nabla-2-dft-a-universal-quantum-chemistry","slug":"nabla-2-dft-a-universal-quantum-chemistry","title":"$\\nabla^2$DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials","date":"2024-06-20","arxiv_id":"2406.14347","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/nabla-2-dft-a-universal-quantum-chemistry#ran","syntology_url":"https://syntology.ai/paper/2406.14347","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.14347"}},"official":{"repos":["AIRI-Institute/nablaDFT"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/moleculargpt-open-large-language-model-llm","slug":"moleculargpt-open-large-language-model-llm","title":"MolecularGPT: Open Large Language Model (LLM) for Few-Shot Molecular Property Prediction","date":"2024-06-18","arxiv_id":"2406.12950","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/moleculargpt-open-large-language-model-llm#ran","syntology_url":"https://syntology.ai/paper/2406.12950","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.12950"}},"official":{"repos":["nyushcs/moleculargpt"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/from-theory-to-therapy-reframing-sbdd-model","slug":"from-theory-to-therapy-reframing-sbdd-model","title":"From Theory to Therapy: Reframing SBDD Model Evaluation via Practical Metrics","date":"2024-06-13","arxiv_id":"2406.08980","repositories_listed":1,"syntology":null},{"url":"/paper/moleculecla-rethinking-molecular-benchmark","slug":"moleculecla-rethinking-molecular-benchmark","title":"MoleculeCLA: Rethinking Molecular Benchmark via Computational Ligand-Target Binding Analysis","date":"2024-06-13","arxiv_id":"2406.17797","repositories_listed":1,"syntology":null},{"url":"/paper/moti-mathcal-ve-a-drug-target-interaction","slug":"moti-mathcal-ve-a-drug-target-interaction","title":"MOTIVE: A Drug-Target Interaction Graph For Inductive Link Prediction","date":"2024-06-12","arxiv_id":"2406.08649","repositories_listed":1,"syntology":null},{"url":"/paper/d-gril-end-to-end-topological-learning-with-2","slug":"d-gril-end-to-end-topological-learning-with-2","title":"D-GRIL: End-to-End Topological Learning with 2-parameter Persistence","date":"2024-06-11","arxiv_id":"2406.07100","repositories_listed":1,"syntology":null},{"url":"/paper/entropy-reinforced-planning-with-large","slug":"entropy-reinforced-planning-with-large","title":"Entropy-Reinforced Planning with Large Language Models for Drug Discovery","date":"2024-06-11","arxiv_id":"2406.07025","repositories_listed":1,"syntology":null},{"url":"/paper/an-open-and-large-scale-dataset-for-multi","slug":"an-open-and-large-scale-dataset-for-multi","title":"An Open and Large-Scale Dataset for Multi-Modal Climate Change-aware Crop Yield Predictions","date":"2024-06-10","arxiv_id":"2406.06081","repositories_listed":1,"syntology":null},{"url":"/paper/smiles2dock-an-open-large-scale-multi-task","slug":"smiles2dock-an-open-large-scale-multi-task","title":"Smiles2Dock: an open large-scale multi-task dataset for ML-based molecular docking","date":"2024-06-09","arxiv_id":"2406.05738","repositories_listed":1,"syntology":null},{"url":"/paper/hyperbolic-benchmarking-unveils-network","slug":"hyperbolic-benchmarking-unveils-network","title":"Hyperbolic Benchmarking Unveils Network Topology-Feature Relationship in GNN Performance","date":"2024-06-04","arxiv_id":"2406.02772","repositories_listed":1,"syntology":null},{"url":"/paper/preference-optimization-for-molecule","slug":"preference-optimization-for-molecule","title":"Preference Optimization for Molecule Synthesis with Conditional Residual Energy-based Models","date":"2024-06-04","arxiv_id":"2406.02066","repositories_listed":1,"syntology":{"n":20,"n_ran":10,"n_constructed":0,"n_ran_checked":6,"n_instrument":4,"n_unverified":10,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 4 where Syntology's instrument failed) · 10 unverified","sample_list":"/paper/preference-optimization-for-molecule#ran","syntology_url":"https://syntology.ai/paper/2406.02066","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.02066"}},"official":{"repos":["songtaoliu0823/crebm"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":10,"ran_from_kinds":["official"]}}},{"url":"/paper/fusiondti-fine-grained-binding-discovery-with","slug":"fusiondti-fine-grained-binding-discovery-with","title":"FusionDTI: Fine-grained Binding Discovery with Token-level Fusion for Drug-Target Interaction","date":"2024-06-03","arxiv_id":"2406.01651","repositories_listed":1,"syntology":null},{"url":"/paper/tagmol-target-aware-gradient-guided-molecule","slug":"tagmol-target-aware-gradient-guided-molecule","title":"TAGMol: Target-Aware Gradient-guided Molecule Generation","date":"2024-06-03","arxiv_id":"2406.01650","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":2,"n_violates":0,"n_no_contract":3,"n_pointer_only":7,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 2 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/tagmol-target-aware-gradient-guided-molecule#ran","syntology_url":"https://syntology.ai/paper/2406.01650","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.01650"}},"official":{"repos":["MoleculeAI/TAGMol"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/full-atom-peptide-design-based-on-multi-modal","slug":"full-atom-peptide-design-based-on-multi-modal","title":"Full-Atom Peptide Design based on Multi-modal Flow Matching","date":"2024-06-02","arxiv_id":"2406.00735","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":2,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/full-atom-peptide-design-based-on-multi-modal#ran","syntology_url":"https://syntology.ai/paper/2406.00735","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.00735"}},"official":{"repos":["Ced3-han/PepFlowww"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/collective-variable-free-transition-path","slug":"collective-variable-free-transition-path","title":"Transition Path Sampling with Improved Off-Policy Training of Diffusion Path Samplers","date":"2024-05-30","arxiv_id":"2405.19961","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":5,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/collective-variable-free-transition-path#ran","syntology_url":"https://syntology.ai/paper/2405.19961","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.19961"}},"official":{"repos":["kiyoung98/tps-dps"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/adapting-differential-molecular","slug":"adapting-differential-molecular","title":"Adapting Differential Molecular Representation with Hierarchical Prompts for Multi-label Property Prediction","date":"2024-05-29","arxiv_id":"2405.18724","repositories_listed":1,"syntology":null},{"url":"/paper/ncidiff-non-covalent-interaction-generative","slug":"ncidiff-non-covalent-interaction-generative","title":"BInD: Bond and Interaction-generating Diffusion Model for Multi-objective Structure-based Drug Design","date":"2024-05-27","arxiv_id":"2405.16861","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-for-protein-ligand-docking-are","slug":"deep-learning-for-protein-ligand-docking-are","title":"Deep Learning for Protein-Ligand Docking: Are We There Yet?","date":"2024-05-23","arxiv_id":"2405.14108","repositories_listed":1,"syntology":null},{"url":"/paper/guided-multi-objective-generative-ai-to","slug":"guided-multi-objective-generative-ai-to","title":"Guided Multi-objective Generative AI to Enhance Structure-based Drug Design","date":"2024-05-20","arxiv_id":"2405.11785","repositories_listed":1,"syntology":null},{"url":"/paper/discrete-state-continuous-time-diffusion-for","slug":"discrete-state-continuous-time-diffusion-for","title":"Discrete-state Continuous-time Diffusion for Graph Generation","date":"2024-05-19","arxiv_id":"2405.11416","repositories_listed":1,"syntology":{"n":13,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":10,"n_pointer_only":13,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/discrete-state-continuous-time-diffusion-for#ran","syntology_url":"https://syntology.ai/paper/2405.11416","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.11416"}},"official":{"repos":["pricexu/disco"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/subgdiff-a-subgraph-diffusion-model-to","slug":"subgdiff-a-subgraph-diffusion-model-to","title":"SubGDiff: A Subgraph Diffusion Model to Improve Molecular Representation Learning","date":"2024-05-09","arxiv_id":"2405.05665","repositories_listed":1,"syntology":null},{"url":"/paper/data-efficient-molecular-generation-with","slug":"data-efficient-molecular-generation-with","title":"Data-Efficient Molecular Generation with Hierarchical Textual Inversion","date":"2024-05-05","arxiv_id":"2405.02845","repositories_listed":1,"syntology":null},{"url":"/paper/contrastive-dual-interaction-graph-neural","slug":"contrastive-dual-interaction-graph-neural","title":"Contrastive Dual-Interaction Graph Neural Network for Molecular Property Prediction","date":"2024-05-04","arxiv_id":"2405.02628","repositories_listed":1,"syntology":null},{"url":"/paper/quality-weighted-vendi-scores-and-their","slug":"quality-weighted-vendi-scores-and-their","title":"Quality-Weighted Vendi Scores And Their Application To Diverse Experimental Design","date":"2024-05-03","arxiv_id":"2405.02449","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/quality-weighted-vendi-scores-and-their#ran","syntology_url":"https://syntology.ai/paper/2405.02449","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.02449"}},"official":{"repos":["vertaix/quality-weighted-vendi-score"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/synflownet-towards-molecule-design-with","slug":"synflownet-towards-molecule-design-with","title":"SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraints","date":"2024-05-02","arxiv_id":"2405.01155","repositories_listed":1,"syntology":{"n":12,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/synflownet-towards-molecule-design-with#ran","syntology_url":"https://syntology.ai/paper/2405.01155","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.01155"}},"official":{"repos":["mirunacrt/synflownet"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/leak-proof-cmap-a-framework-for-training-and","slug":"leak-proof-cmap-a-framework-for-training-and","title":"Leak Proof CMap; a framework for training and evaluation of cell line agnostic L1000 similarity methods","date":"2024-04-29","arxiv_id":"2404.18960","repositories_listed":1,"syntology":null},{"url":"/paper/revold-ultra-large-library-screening-with-an","slug":"revold-ultra-large-library-screening-with-an","title":"REvoLd: Ultra-Large Library Screening with an Evolutionary Algorithm in Rosetta","date":"2024-04-26","arxiv_id":"2404.17329","repositories_listed":1,"syntology":null},{"url":"/paper/atomas-hierarchical-alignment-on-molecule","slug":"atomas-hierarchical-alignment-on-molecule","title":"Atomas: Hierarchical Alignment on Molecule-Text for Unified Molecule Understanding and Generation","date":"2024-04-23","arxiv_id":"2404.16880","repositories_listed":1,"syntology":null},{"url":"/paper/fmint-bridging-human-designed-and-data","slug":"fmint-bridging-human-designed-and-data","title":"FMint: Bridging Human Designed and Data Pretrained Models for Differential Equation Foundation Model","date":"2024-04-23","arxiv_id":"2404.14688","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fmint-bridging-human-designed-and-data#ran","syntology_url":"https://syntology.ai/paper/2404.14688","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.14688"}},"official":{"repos":["margotyjx/fmint"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/accelerating-the-generation-of-molecular","slug":"accelerating-the-generation-of-molecular","title":"Accelerating the Generation of Molecular Conformations with Progressive Distillation of Equivariant Latent Diffusion Models","date":"2024-04-21","arxiv_id":"2404.13491","repositories_listed":1,"syntology":null},{"url":"/paper/masked-autoencoders-for-microscopy-are","slug":"masked-autoencoders-for-microscopy-are","title":"Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology","date":"2024-04-16","arxiv_id":"2404.10242","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/masked-autoencoders-for-microscopy-are#ran","syntology_url":"https://syntology.ai/paper/2404.10242","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.10242"}},"official":{"repos":["recursionpharma/maes_microscopy"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-self-feedback-knowledge-elicitation","slug":"a-self-feedback-knowledge-elicitation","title":"A Self-feedback Knowledge Elicitation Approach for Chemical Reaction Predictions","date":"2024-04-15","arxiv_id":"2404.09606","repositories_listed":1,"syntology":null},{"url":"/paper/transformers-for-molecular-property","slug":"transformers-for-molecular-property","title":"Transformers for molecular property prediction: Lessons learned from the past five years","date":"2024-04-05","arxiv_id":"2404.03969","repositories_listed":1,"syntology":null},{"url":"/paper/drug-target-interaction-prediction-by","slug":"drug-target-interaction-prediction-by","title":"Drug-target interaction prediction by integrating heterogeneous information with mutual attention network","date":"2024-04-03","arxiv_id":"2404.03516","repositories_listed":1,"syntology":null},{"url":"/paper/fabind-enhancing-molecular-docking-through","slug":"fabind-enhancing-molecular-docking-through","title":"FABind+: Enhancing Molecular Docking through Improved Pocket Prediction and Pose Generation","date":"2024-03-29","arxiv_id":"2403.20261","repositories_listed":1,"syntology":null},{"url":"/paper/mol-air-molecular-reinforcement-learning-with","slug":"mol-air-molecular-reinforcement-learning-with","title":"Mol-AIR: Molecular Reinforcement Learning with Adaptive Intrinsic Rewards for Goal-directed Molecular Generation","date":"2024-03-29","arxiv_id":"2403.20109","repositories_listed":1,"syntology":null},{"url":"/paper/a-python-library-for-efficient-computation-of","slug":"a-python-library-for-efficient-computation-of","title":"A Python library for efficient computation of molecular fingerprints","date":"2024-03-27","arxiv_id":"2403.19718","repositories_listed":1,"syntology":null},{"url":"/paper/grad-camo-learning-interpretable-single-cell","slug":"grad-camo-learning-interpretable-single-cell","title":"Grad-CAMO: Learning Interpretable Single-Cell Morphological Profiles from 3D Cell Painting Images","date":"2024-03-26","arxiv_id":"2403.17615","repositories_listed":1,"syntology":null},{"url":"/paper/nana-and-migu-semantic-data-augmentation","slug":"nana-and-migu-semantic-data-augmentation","title":"NaNa and MiGu: Semantic Data Augmentation Techniques to Enhance Protein Classification in Graph Neural Networks","date":"2024-03-21","arxiv_id":"2403.14736","repositories_listed":1,"syntology":null},{"url":"/paper/instruction-multi-constraint-molecular","slug":"instruction-multi-constraint-molecular","title":"Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language Model","date":"2024-03-20","arxiv_id":"2403.13244","repositories_listed":1,"syntology":null},{"url":"/paper/forward-learning-of-graph-neural-networks","slug":"forward-learning-of-graph-neural-networks","title":"Forward Learning of Graph Neural Networks","date":"2024-03-16","arxiv_id":"2403.11004","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/forward-learning-of-graph-neural-networks#ran","syntology_url":"https://syntology.ai/paper/2403.11004","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.11004"}},"official":{"repos":["facebookresearch/forwardgnn"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/an-improved-metric-and-benchmark-for","slug":"an-improved-metric-and-benchmark-for","title":"An Improved Metric and Benchmark for Assessing the Performance of Virtual Screening Models","date":"2024-03-15","arxiv_id":"2403.10478","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/an-improved-metric-and-benchmark-for#ran","syntology_url":"https://syntology.ai/paper/2403.10478","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.10478"}},"official":{"repos":["molecularmodelinglab/bigbind"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/cardiogenai-a-machine-learning-based","slug":"cardiogenai-a-machine-learning-based","title":"CardioGenAI: A Machine Learning-Based Framework for Re-Engineering Drugs for Reduced hERG Liability","date":"2024-03-12","arxiv_id":"2403.07632","repositories_listed":1,"syntology":null},{"url":"/paper/generative-deep-learning-enabled-ultra-large","slug":"generative-deep-learning-enabled-ultra-large","title":"Physics-informed generative real-time lens-free imaging","date":"2024-03-12","arxiv_id":"2403.07786","repositories_listed":1,"syntology":null},{"url":"/paper/3m-diffusion-latent-multi-modal-diffusion-for","slug":"3m-diffusion-latent-multi-modal-diffusion-for","title":"3M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure Generation","date":"2024-03-11","arxiv_id":"2403.07179","repositories_listed":1,"syntology":null},{"url":"/paper/gnn-vpa-a-variance-preserving-aggregation","slug":"gnn-vpa-a-variance-preserving-aggregation","title":"GNN-VPA: A Variance-Preserving Aggregation Strategy for Graph Neural Networks","date":"2024-03-07","arxiv_id":"2403.04747","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/gnn-vpa-a-variance-preserving-aggregation#ran","syntology_url":"https://syntology.ai/paper/2403.04747","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.04747"}},"official":{"repos":["ml-jku/gnn-vpa"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/confidence-on-the-focal-conformal-prediction","slug":"confidence-on-the-focal-conformal-prediction","title":"Confidence on the Focal: Conformal Prediction with Selection-Conditional Coverage","date":"2024-03-06","arxiv_id":"2403.03868","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/confidence-on-the-focal-conformal-prediction#ran","syntology_url":"https://syntology.ai/paper/2403.03868","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.03868"}},"official":{"repos":["ying531/jomi-paper"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/ppflow-target-aware-peptide-design-with","slug":"ppflow-target-aware-peptide-design-with","title":"PPFlow: Target-aware Peptide Design with Torsional Flow Matching","date":"2024-03-05","arxiv_id":"2405.06642","repositories_listed":1,"syntology":null},{"url":"/paper/sciassess-benchmarking-llm-proficiency-in","slug":"sciassess-benchmarking-llm-proficiency-in","title":"SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis","date":"2024-03-04","arxiv_id":"2403.01976","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/sciassess-benchmarking-llm-proficiency-in#ran","syntology_url":"https://syntology.ai/paper/2403.01976","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.01976"}},"official":{"repos":["sci-assess/sciassess"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/biot5-towards-generalized-biological","slug":"biot5-towards-generalized-biological","title":"BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning","date":"2024-02-27","arxiv_id":"2402.17810","repositories_listed":1,"syntology":null},{"url":"/paper/text-guided-molecule-generation-with","slug":"text-guided-molecule-generation-with","title":"Text-Guided Molecule Generation with Diffusion Language Model","date":"2024-02-20","arxiv_id":"2402.13040","repositories_listed":1,"syntology":{"n":21,"n_ran":14,"n_constructed":0,"n_ran_checked":8,"n_instrument":6,"n_unverified":7,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":21,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 6 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/text-guided-molecule-generation-with#ran","syntology_url":"https://syntology.ai/paper/2402.13040","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.13040"}},"official":{"repos":["deno-v/tgm-dlm"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/llasmol-advancing-large-language-models-for","slug":"llasmol-advancing-large-language-models-for","title":"LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale, Comprehensive, High-Quality Instruction Tuning Dataset","date":"2024-02-14","arxiv_id":"2402.09391","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/llasmol-advancing-large-language-models-for#ran","syntology_url":"https://syntology.ai/paper/2402.09391","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.09391"}},"official":{"repos":["osu-nlp-group/llm4chem"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/psc-cpi-multi-scale-protein-sequence","slug":"psc-cpi-multi-scale-protein-sequence","title":"PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for Efficient and Generalizable Compound-Protein Interaction Prediction","date":"2024-02-13","arxiv_id":"2402.08198","repositories_listed":1,"syntology":{"n":12,"n_ran":7,"n_constructed":3,"n_ran_checked":7,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 3 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/psc-cpi-multi-scale-protein-sequence#ran","syntology_url":"https://syntology.ai/paper/2402.08198","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.08198"}},"official":{"repos":["lirongwu/psc-cpi"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":3,"n_ran_no_instrument_failure":7,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/quantum-computing-enhanced-algorithm-unveils","slug":"quantum-computing-enhanced-algorithm-unveils","title":"Quantum Computing-Enhanced Algorithm Unveils Novel Inhibitors for KRAS","date":"2024-02-13","arxiv_id":"2402.08210","repositories_listed":1,"syntology":null},{"url":"/paper/plapt-protein-ligand-binding-affinity","slug":"plapt-protein-ligand-binding-affinity","title":"PLAPT: Protein-Ligand Binding Affinity Prediction Using Pretrained Transformers","date":"2024-02-08","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/unveiling-molecular-moieties-through","slug":"unveiling-molecular-moieties-through","title":"Unveiling Molecular Moieties through Hierarchical Grad-CAM Graph Explainability","date":"2024-01-29","arxiv_id":"2402.01744","repositories_listed":1,"syntology":null},{"url":"/paper/a-multi-grained-symmetric-differential","slug":"a-multi-grained-symmetric-differential","title":"A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics","date":"2024-01-26","arxiv_id":"2401.15122","repositories_listed":1,"syntology":null},{"url":"/paper/improving-antibody-humanness-prediction-using","slug":"improving-antibody-humanness-prediction-using","title":"Improving Antibody Humanness Prediction using Patent Data","date":"2024-01-25","arxiv_id":"2401.14442","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/improving-antibody-humanness-prediction-using#ran","syntology_url":"https://syntology.ai/paper/2401.14442","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.14442"}},"official":{"repos":["astrazeneca/selfpad"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/inverse-molecular-design-with-multi","slug":"inverse-molecular-design-with-multi","title":"Graph Diffusion Transformers for Multi-Conditional Molecular Generation","date":"2024-01-24","arxiv_id":"2401.13858","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":8,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/inverse-molecular-design-with-multi#ran","syntology_url":"https://syntology.ai/paper/2401.13858","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.13858"}},"official":{"repos":["liugangcode/MCD"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/moltailor-tailoring-chemical-molecular","slug":"moltailor-tailoring-chemical-molecular","title":"MolTailor: Tailoring Chemical Molecular Representation to Specific Tasks via Text Prompts","date":"2024-01-21","arxiv_id":"2401.11403","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/moltailor-tailoring-chemical-molecular#ran","syntology_url":"https://syntology.ai/paper/2401.11403","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.11403"}},"official":{"repos":["scir-hi/moltailor"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/pepharmony-a-multi-view-contrastive-learning","slug":"pepharmony-a-multi-view-contrastive-learning","title":"PepHarmony: A Multi-View Contrastive Learning Framework for Integrated Sequence and Structure-Based Peptide Encoding","date":"2024-01-21","arxiv_id":"2401.11360","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pepharmony-a-multi-view-contrastive-learning#ran","syntology_url":"https://syntology.ai/paper/2401.11360","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.11360"}},"official":{"repos":["zhangruochi/pepharmony"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/fimba-evaluating-the-robustness-of-ai-in","slug":"fimba-evaluating-the-robustness-of-ai-in","title":"FIMBA: Evaluating the Robustness of AI in Genomics via Feature Importance Adversarial Attacks","date":"2024-01-19","arxiv_id":"2401.10657","repositories_listed":1,"syntology":null},{"url":"/paper/empirical-evidence-for-the-fragment-level","slug":"empirical-evidence-for-the-fragment-level","title":"Empirical Evidence for the Fragment level Understanding on Drug Molecular Structure of LLMs","date":"2024-01-15","arxiv_id":"2401.07657","repositories_listed":1,"syntology":null},{"url":"/paper/twinbooster-synergising-large-language-models","slug":"twinbooster-synergising-large-language-models","title":"TwinBooster: Synergising Large Language Models with Barlow Twins and Gradient Boosting for Enhanced Molecular Property Prediction","date":"2024-01-09","arxiv_id":"2401.04478","repositories_listed":1,"syntology":null},{"url":"/paper/multi-modal-representation-learning-for-1","slug":"multi-modal-representation-learning-for-1","title":"Multi-Modal Representation Learning for Molecular Property Prediction: Sequence, Graph, Geometry","date":"2024-01-07","arxiv_id":"2401.03369","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/multi-modal-representation-learning-for-1#ran","syntology_url":"https://syntology.ai/paper/2401.03369","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.03369"}},"official":{"repos":["vencent-won/sggrl"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/uncertainty-quantification-on-clinical-trial","slug":"uncertainty-quantification-on-clinical-trial","title":"Uncertainty Quantification on Clinical Trial Outcome Prediction","date":"2024-01-07","arxiv_id":"2401.03482","repositories_listed":1,"syntology":null},{"url":"/paper/drugassist-a-large-language-model-for","slug":"drugassist-a-large-language-model-for","title":"DrugAssist: A Large Language Model for Molecule Optimization","date":"2023-12-28","arxiv_id":"2401.10334","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/drugassist-a-large-language-model-for#ran","syntology_url":"https://syntology.ai/paper/2401.10334","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.10334"}},"official":{"repos":["blazerye/drugassist"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/dtiam-a-unified-framework-for-predicting-drug","slug":"dtiam-a-unified-framework-for-predicting-drug","title":"DTIAM: A unified framework for predicting drug-target interactions, binding affinities and activation/inhibition mechanisms","date":"2023-12-23","arxiv_id":"2312.15252","repositories_listed":1,"syntology":null},{"url":"/paper/principled-weight-initialisation-for-input-1","slug":"principled-weight-initialisation-for-input-1","title":"Principled Weight Initialisation for Input-Convex Neural Networks","date":"2023-12-19","arxiv_id":"2312.12474","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/principled-weight-initialisation-for-input-1#ran","syntology_url":"https://syntology.ai/paper/2312.12474","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.12474"}},"official":{"repos":["ml-jku/convex-init"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/cldr-contrastive-learning-drug-response","slug":"cldr-contrastive-learning-drug-response","title":"CLDR: Contrastive Learning Drug Response Models from Natural Language Supervision","date":"2023-12-17","arxiv_id":"2312.10707","repositories_listed":1,"syntology":null},{"url":"/paper/phendiff-revealing-invisible-phenotypes-with","slug":"phendiff-revealing-invisible-phenotypes-with","title":"PhenDiff: Revealing Subtle Phenotypes with Diffusion Models in Real Images","date":"2023-12-13","arxiv_id":"2312.08290","repositories_listed":1,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":8,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/phendiff-revealing-invisible-phenotypes-with#ran","syntology_url":"https://syntology.ai/paper/2312.08290","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.08290"}},"official":{"repos":["warmongeringbeaver/phendiff"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":3,"ran_from_kinds":["official"]}}}],"record_sha256":"bd9474388c277830ade9236a601840c213e677fa52d11d126718cf3f7c28642f","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}