{"about":{"site":"https://codewithpapers.app","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.","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"},"url":"/paper/uncertainty-quantification-using-neural","title":"Uncertainty Quantification Using Neural Networks for Molecular Property Prediction","arxiv_id":"2005.10036","date":"2020-05-20","proceeding":null,"authors":["Lior Hirschfeld","Kyle Swanson","Kevin Yang","Regina Barzilay","Connor W. Coley"],"abstract":"Uncertainty quantification (UQ) is an important component of molecular property prediction, particularly for drug discovery applications where model predictions direct experimental design and where unanticipated imprecision wastes valuable time and resources. The need for UQ is especially acute for neural models, which are becoming increasingly standard yet are challenging to interpret. While several approaches to UQ have been proposed in the literature, there is no clear consensus on the comparative performance of these models. In this paper, we study this question in the context of regression tasks. We systematically evaluate several methods on five benchmark datasets using multiple complementary performance metrics. Our experiments show that none of the methods we tested is unequivocally superior to all others, and none produces a particularly reliable ranking of errors across multiple datasets. While we believe these results show that existing UQ methods are not sufficient for all common use-cases and demonstrate the benefits of further research, we conclude with a practical recommendation as to which existing techniques seem to perform well relative to others.","url_abs":"https://arxiv.org/abs/2005.10036v1","url_pdf":"https://arxiv.org/pdf/2005.10036v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"uncertainty-quantification-using-neural","repo_url":"https://github.com/lhirschfeld/ChempropUncertaintyQuantification","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"drug-discovery","task_name":"Drug Discovery"},{"task_slug":"experimental-design","task_name":"Experimental Design"},{"task_slug":"molecular-property-prediction","task_name":"Molecular Property Prediction"},{"task_slug":"property-prediction","task_name":"Property Prediction"},{"task_slug":"uncertainty-quantification","task_name":"Uncertainty Quantification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2005.10036","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2005.10036"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/lhirschfeld/ChempropUncertaintyQuantification","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":6},"by_repo_kind":{"official":{"samples":6,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"ca7675a6350f0967","entry":"compute_gnorm","repo":"lhirschfeld/ChempropUncertaintyQuantification","repo_kind":"official","path":"chemprop/nn_utils.py","file_url":"https://github.com/lhirschfeld/ChempropUncertaintyQuantification/blob/HEAD/chemprop/nn_utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"ca7675a6350f0967"}},{"code_sha256_prefix":"e415ae10bb5a4e3b","entry":"compute_pnorm","repo":"lhirschfeld/ChempropUncertaintyQuantification","repo_kind":"official","path":"chemprop/nn_utils.py","file_url":"https://github.com/lhirschfeld/ChempropUncertaintyQuantification/blob/HEAD/chemprop/nn_utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"e415ae10bb5a4e3b"}},{"code_sha256_prefix":"76e27ba5baa89e9f","entry":"load_args","repo":"lhirschfeld/ChempropUncertaintyQuantification","repo_kind":"official","path":"chemprop/utils.py","file_url":"https://github.com/lhirschfeld/ChempropUncertaintyQuantification/blob/HEAD/chemprop/utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"76e27ba5baa89e9f"}},{"code_sha256_prefix":"d68ff6a82a950b49","entry":"param_count","repo":"lhirschfeld/ChempropUncertaintyQuantification","repo_kind":"official","path":"chemprop/nn_utils.py","file_url":"https://github.com/lhirschfeld/ChempropUncertaintyQuantification/blob/HEAD/chemprop/nn_utils.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"d68ff6a82a950b49"}},{"code_sha256_prefix":"8549c6706d4b4ef2","entry":"params_to_line","repo":"lhirschfeld/ChempropUncertaintyQuantification","repo_kind":"official","path":"uncertainty_evaluation/populate_build.py","file_url":"https://github.com/lhirschfeld/ChempropUncertaintyQuantification/blob/HEAD/uncertainty_evaluation/populate_build.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"8549c6706d4b4ef2"}},{"code_sha256_prefix":"2d5729b2a7419c74","entry":"train_residual_model","repo":"lhirschfeld/ChempropUncertaintyQuantification","repo_kind":"official","path":"chemprop/models/residual_models.py","file_url":"https://github.com/lhirschfeld/ChempropUncertaintyQuantification/blob/HEAD/chemprop/models/residual_models.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"2d5729b2a7419c74"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}