The QDπ dataset, training data for drug-like molecules and biopolymer fragments and their interactions
Year: 2025
Type: data-paper
Abstract: The development of universal machine learning potentials (MLP) for small organic and drug-like molecules requires large, accurate datasets that span diverse chemical spaces. In this study, we introduce the QDπ dataset which incorporates data taken from several datasets. We use a query-by-committee active learning strategy to extract data from large datasets to maximize the diversity and avoid redundancy as relevant for neural network training to construct the QDπ dataset. The QDπ dataset requires only 1.6 million structures to express the chemical diversity of 13 elements from the various source datasets at the ωB97M-D3(BJ)/def2-TZVPPD level of theory. The QDπ dataset enables creation of flexible target loss functions for neural network training relevant to drug discovery, including information-dense data sets of relative conformational energies and barriers, intermolecular interactions, tautomers and relative protonation energies of drug-like compounds and biomolecular fragments. It is the hope that the high chemical information density and diversity contained in the QDπ dataset will provide a valuable resource for the development of new universal MLPs for drug discovery.
Source: Scientific Data
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,490
Funders: National Science Foundation, U.S. Department of Health and Human Services, San Diego Supercomputer Center, National Institutes of Health, Office of Advanced Research Computing, Rutgers, The State University of New Jersey, National Institute of General Medical Sciences, Office of Advanced Cyberinfrastructure, Division of Chemistry
Awards: Collaborative Research: Frameworks: Interoperable High-Performance Classical, Machine Learning and Quantum Free Energy Methods in AMBER, G1453181110, G1454711611, G1555197637, G1787346651, Track 4: Advanced CI Coordination Ecosystem: Monitoring and Measurement Services, G2293942063, G338811424, G34258105, G3562386289, Track 1: ACCESS Resource Allocations Marketplace and Platform Services (RAMPS), G4010157413, G4275568664, Track 3: COre National Ecosystem for CyberinfrasTructure (CONECT), G5156331529, G5367127336, G5464919783, Collaborative Research: Frameworks: Interoperable High-Performance Classical, Machine Learning and Quantum Free Energy Methods in AMBER, Next-generation alchemical free energy methods and quantum/machine-learning models for drug discovery, G7526614437, G7916508619, Track 2: Customized Multi-tier Assistance, Training, and Computational Help (MATCH) for End User ACCESS to CI, G8499740153, G8854745445, G8994211811, G9000185615
OpenAlex ID: W4409826630