{"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/pocket2mol-efficient-molecular-sampling-based","title":"Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets","arxiv_id":"2205.07249","date":"2022-05-15","proceeding":null,"authors":["Xingang Peng","Shitong Luo","Jiaqi Guan","Qi Xie","Jian Peng","Jianzhu Ma"],"abstract":"Deep generative models have achieved tremendous success in designing novel drug molecules in recent years. A new thread of works have shown the great potential in advancing the specificity and success rate of in silico drug design by considering the structure of protein pockets. This setting posts fundamental computational challenges in sampling new chemical compounds that could satisfy multiple geometrical constraints imposed by pockets. Previous sampling algorithms either sample in the graph space or only consider the 3D coordinates of atoms while ignoring other detailed chemical structures such as bond types and functional groups. To address the challenge, we develop Pocket2Mol, an E(3)-equivariant generative network composed of two modules: 1) a new graph neural network capturing both spatial and bonding relationships between atoms of the binding pockets and 2) a new efficient algorithm which samples new drug candidates conditioned on the pocket representations from a tractable distribution without relying on MCMC. Experimental results demonstrate that molecules sampled from Pocket2Mol achieve significantly better binding affinity and other drug properties such as druglikeness and synthetic accessibility.","url_abs":"https://arxiv.org/abs/2205.07249v1","url_pdf":"https://arxiv.org/pdf/2205.07249v1.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":"pocket2mol-efficient-molecular-sampling-based","repo_url":"https://github.com/pengxingang/pocket2mol","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"pocket2mol-efficient-molecular-sampling-based","repo_url":"https://github.com/guanjq/targetdiff","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"pocket2mol-efficient-molecular-sampling-based","repo_url":"https://github.com/luost26/3d-generative-sbdd","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"pocket2mol-efficient-molecular-sampling-based","repo_url":"https://github.com/yanliang3612/nucleusdiff","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"drug-design","task_name":"Drug Design"},{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"},{"task_slug":"specificity","task_name":"Specificity"}],"methods":[{"method_slug":"graph-neural-network","method_name":"Graph Neural Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2205.07249","atlas_url":"https://app.syntology.ai/?focus=2205.07249","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.07249"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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":"deterministic:regex_extraction","url":"https://github.com/pengxingang/Pocket2Mol","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/guanjq/targetdiff","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/pengxingang/pocket2mol","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/luost26/3d-generative-sbdd","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/yanliang3612/nucleusdiff","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran_violates":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"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":"f7f4febb616f0a89","entry":"logp_to_rank_prob","repo":"pengxingang/Pocket2Mol","repo_kind":"official","path":"sample.py","file_url":"https://github.com/pengxingang/Pocket2Mol/blob/HEAD/sample.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"f7f4febb616f0a89"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}