Physics-informed generative model for drug-like molecule conformers
<|reference_start|>Physics-informed generative model for drug-like molecule conformers: We present a diffusion-based, generative model for conformer generation. Our model is focused on the reproduction of bonded structure and is constructed from the associated terms traditionally found in classical force fields to ensure a physically relevant representation. Techniques in deep learning are used to infer atom typing and geometric parameters from a training set. Conformer sampling is achieved by taking advantage of recent advancements in diffusion-based generation. By training on large, synthetic data sets of diverse, drug-like molecules optimized with the semiempirical GFN2-xTB method, high accuracy is achieved for bonded parameters, exceeding that of conventional, knowledge-based methods. Results are also compared to experimental structures from the Protein Databank (PDB) and Cambridge Structural Database (CSD).<|reference_end|>
@article{williams2024physics-informed,
    title={Physics-informed generative model for drug-like molecule conformers},
    author={David C. Williams and Neil Inala},
    journal={arXiv preprint arXiv:2403.07925},
    year={2024},
    doi={10.1021/acs.jcim.3c01816},
    archivePrefix={arXiv},
    eprint={2403.07925},
    primaryClass={q-bio.BM cs.LG physics.chem-ph}
}