Year: 2025
Type: preprint
Abstract: Molecular geometry is crucial for biological activity and chemical reactivity; however, computational methods for generating 3D structures are limited by the vast scale of conformational space and the complexities of stereochemistry. Here we present an approach that combines an expansive dataset of molecular conformers with generative diffusion models to address this problem. We introduce ChEMBL3D, which contains over 250 million molecular geometries for 1.8 million drug-like compounds, optimized using AIMNet2 neural network potentials to a near-quantum mechanical accuracy with implicit solvent effects included. This dataset captures complex organic molecules in various protonation states and stereochemical configurations. We then developed LoQI, a stereochemistry-aware diffusion model that learns molecular geometry distributions directly from this data. Through graph augmentation, LoQI accurately generates molecular structures with targeted stereochemistry, representing a significant advance in modeling capabilities over previous generative methods. The model outperforms traditional approaches, achieving up to tenfold improvements in energy accuracy and effective recovery of optimal conformations. Benchmark tests on complex systems, including macrocycles and flexible molecules, as well as validation with crystal structures, show LoQI can perform low energy conformer search efficiently. The model code and dataset are available at https: //github.com/isayevlab/LoQI.
Source: ChemRxiv
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G1453181110, G1555197637, G1787346651, Track 4: Advanced CI Coordination Ecosystem: Monitoring and Measurement Services, G2065323065, G2336192781, G338811424, G3562386289, Track 1: ACCESS Resource Allocations Marketplace and Platform Services (RAMPS), G4010157413, Track 3: COre National Ecosystem for CyberinfrasTructure (CONECT), G5367127336, G5464919783, G6291097390, G7916508619, Track 2: Customized Multi-tier Assistance, Training, and Computational Help (MATCH) for End User ACCESS to CI, Collaborative Research: A Data-driven Closed-loop Framework for De Novo Generation of Molecules with Targeted Properties, G8994211811
OpenAlex ID: W4413356074