{"entity": "journal", "iuid": "1421d15f43604d0798b75b5c18723beb", "timestamp": "2026-08-17T01:32:37.120Z", "links": {"self": {"href": "https://publications.scilifelab.se/journal/Nat%20Comput%20Sci.json"}, "display": {"href": "https://publications.scilifelab.se/journal/Nat%20Comput%20Sci"}}, "title": "Nat Comput Sci", "issn": "2662-8457", "issn-l": null, "publications_count": 2, "publications": [{"entity": "publication", "iuid": "33f7c1d27c724a188e43c1e941c7eaac", "links": {"self": {"href": "https://publications.scilifelab.se/publication/33f7c1d27c724a188e43c1e941c7eaac.json"}, "display": {"href": "https://publications.scilifelab.se/publication/33f7c1d27c724a188e43c1e941c7eaac"}}, "title": "Rapid traversal of vast chemical space using machine learning-guided docking screens.", "authors": [{"family": "Luttens", "given": "Andreas", "initials": "A", "orcid": "0000-0003-2915-7901", "researcher": {"href": "https://publications.scilifelab.se/researcher/7d46047fab4247caaeecf31be6da987f.json"}}, {"family": "Cabeza de Vaca", "given": "Israel", "initials": "I"}, {"family": "Sparring", "given": "Leonard", "initials": "L"}, {"family": "Brea", "given": "Jos\u00e9", "initials": "J"}, {"family": "Mart\u00ednez", "given": "Ant\u00f3n Leandro", "initials": "AL", "orcid": "0000-0002-1595-3459", "researcher": {"href": "https://publications.scilifelab.se/researcher/46b781fca7af46b49699abf9f063c390.json"}}, {"family": "Kahlous", "given": "Nour Aldin", "initials": "NA", "orcid": "0000-0002-7744-1491", "researcher": {"href": "https://publications.scilifelab.se/researcher/f7e3d259da7a4b6d97973a0de8b3d497.json"}}, {"family": "Radchenko", "given": "Dmytro S", "initials": "DS", "orcid": "0000-0001-5444-7754", "researcher": {"href": "https://publications.scilifelab.se/researcher/df4077c366bc4f4a8b422762c0be9cfe.json"}}, {"family": "Moroz", "given": "Yurii S", "initials": "YS", "orcid": "0000-0001-6073-002X", "researcher": {"href": "https://publications.scilifelab.se/researcher/4f8586d3b69b4782bed34192411a02e5.json"}}, {"family": "Loza", "given": "Mar\u00eda Isabel", "initials": "MI", "orcid": "0000-0003-4730-0863", "researcher": {"href": "https://publications.scilifelab.se/researcher/16ba410826ea43aaa8e14e8bb14e6fac.json"}}, {"family": "Norinder", "given": "Ulf", "initials": "U", "orcid": "0000-0003-3107-331X", "researcher": {"href": "https://publications.scilifelab.se/researcher/ecd436012ccc4e29bd07b730fbda51c3.json"}}, {"family": "Carlsson", "given": "Jens", "initials": "J", "orcid": "0000-0003-4623-2977", "researcher": {"href": "https://publications.scilifelab.se/researcher/d7d91087358e46e38bb1b7110dc0b214.json"}}], "type": "journal article", "published": "2025-04-00", "journal": {"title": "Nat Comput Sci", "issn": "2662-8457", "volume": "5", "issue": "4", "pages": "301-312", "issn-l": null}, "abstract": "The accelerating growth of make-on-demand chemical libraries provides unprecedented opportunities to identify starting points for drug discovery with virtual screening. However, these multi-billion-scale libraries are challenging to screen, even for the fastest structure-based docking methods. Here we explore a strategy that combines machine learning and molecular docking to enable rapid virtual screening of databases containing billions of compounds. In our workflow, a classification algorithm is trained to identify top-scoring compounds based on molecular docking of 1 million compounds to the target protein. The conformal prediction framework is then used to make selections from the multi-billion-scale library, reducing the number of compounds to be scored by docking. The CatBoost classifier showed an optimal balance between speed and accuracy and was used to adapt the workflow for screens of ultralarge libraries. Application to a library of 3.5 billion compounds demonstrated that our protocol can reduce the computational cost of structure-based virtual screening by more than 1,000-fold. Experimental testing of predictions identified ligands of G protein-coupled receptors and demonstrated that our approach enables discovery of compounds with multi-target activity tailored for therapeutic effect.", "doi": "10.1038/s43588-025-00777-x", "pmid": "40082701", "labels": {"Bioinformatics Support for Computational Resources": "Service"}, "xrefs": [{"db": "pmc", "key": "PMC12021657"}, {"db": "pii", "key": "10.1038/s43588-025-00777-x"}], "notes": [], "created": "2025-11-28T10:47:46.133Z", "modified": "2025-11-28T10:47:46.396Z"}, {"entity": "publication", "iuid": "aa929085914c406a86ecd4074e8e28b5", "links": {"self": {"href": "https://publications.scilifelab.se/publication/aa929085914c406a86ecd4074e8e28b5.json"}, "display": {"href": "https://publications.scilifelab.se/publication/aa929085914c406a86ecd4074e8e28b5"}}, "title": "MassiveFold: unveiling AlphaFold's hidden potential with optimized and parallelized massive sampling.", "authors": [{"family": "Raouraoua", "given": "Nessim", "initials": "N", "orcid": "0009-0005-5652-684X", "researcher": {"href": "https://publications.scilifelab.se/researcher/95499cc13f5f4ff8b5e688b88d99bbb0.json"}}, {"family": "Mirabello", "given": "Claudio", "initials": "C", "orcid": "0000-0001-7868-034X", "researcher": {"href": "https://publications.scilifelab.se/researcher/00052b54a3d24fd4a6e648f987d15e5f.json"}}, {"family": "V\u00e9ry", "given": "Thibaut", "initials": "T"}, {"family": "Blanchet", "given": "Christophe", "initials": "C"}, {"family": "Wallner", "given": "Bj\u00f6rn", "initials": "B", "orcid": "0000-0002-3772-8279", "researcher": {"href": "https://publications.scilifelab.se/researcher/108086b7b06e4247b332ff4a119b97a5.json"}}, {"family": "Lensink", "given": "Marc F", "initials": "MF", "orcid": "0000-0003-3957-9470", "researcher": {"href": "https://publications.scilifelab.se/researcher/e7f27d0f7d8b407dbc9a8fdd13622248.json"}}, {"family": "Brysbaert", "given": "Guillaume", "initials": "G", "orcid": "0000-0002-6807-6621", "researcher": {"href": "https://publications.scilifelab.se/researcher/74499da411614b84bea0d7c8986cd27e.json"}}], "type": "journal article", "published": "2024-11-11", "journal": {"title": "Nat Comput Sci", "issn": "2662-8457", "issn-l": null}, "abstract": "Massive sampling in AlphaFold enables access to increased structural diversity. In combination with its efficient confidence ranking, this unlocks elevated modeling capabilities for monomeric structures and foremost for protein assemblies. However, the approach struggles with GPU cost and data storage. Here we introduce MassiveFold, an optimized and customizable version of AlphaFold that runs predictions in parallel, reducing the computing time from several months to hours. MassiveFold is scalable and able to run on anything from a single computer to a large GPU infrastructure, where it can fully benefit from all the computing nodes.", "doi": "10.1038/s43588-024-00714-4", "pmid": "39528570", "labels": {"Bioinformatics Support, Infrastructure and Training": "Technology development", "Bioinformatics (NBIS)": "Technology development"}, "xrefs": [{"db": "pii", "key": "10.1038/s43588-024-00714-4"}], "notes": [], "created": "2024-11-18T22:11:25.912Z", "modified": "2024-11-18T22:11:28.574Z"}], "created": "2024-11-18T22:11:28.525Z", "modified": "2024-11-18T22:11:28.525Z"}