We present a formalism of a neural network encoding bonded interactions in molecules. This intramolecular encoding is consistent with the models of intermolecular interactions previously designed by this group. Variants of the encoding fed into a cor...
The development of deep learning models for predicting toxicological endpoints has shown great promise, but one of the challenges in the field is the accuracy and interpretability of these models. The bioactive conformation of a compound plays a crit...
Journal of chemical information and modeling
38587510
Understanding the energetic landscapes of large molecules is necessary for the study of chemical and biological systems. Recently, deep learning has greatly accelerated the development of models based on quantum chemistry, making it possible to build...
Journal of computer-aided molecular design
39052103
Nonadditivity (NA) in Structure-Activity and Structure-Property Relationship (SAR) data is a rare but very information rich phenomenon. It can indicate conformational flexibility, structural rearrangements, and errors in assay results and structural ...
The molecular representation model is a neural network that converts molecular representations (SMILES, Graph) into feature vectors, and is an essential module applied across a wide range of artificial intelligence-driven drug discovery scenarios. Ho...
MOTIVATION: Molecular representation learning is pivotal for advancing deep learning applications in quantum chemistry and drug discovery. Existing methods for molecular representation learning often fall short of fully capturing the intricate intera...
Journal of chemical information and modeling
39565928
Conformer ranking is a crucial task for drug discovery, with methods for generating conformers often based on molecular (meta)dynamics or sophisticated sampling techniques. These methods are constrained by the underlying force computation regarding r...
Chemphyschem : a European journal of chemical physics and physical chemistry
40017058
Accurate and efficient prediction of high energy ligand conformations is important in structure-based drug discovery for the exclusion of unrealistic structures in docking-based virtual screening and de novo design approaches. In this work, we constr...
Journal of chemical information and modeling
39961016
Starting from Merck Molecular Force Field (MMFF) geometries, a neural-net based model has been formulated to closely reproduce ωB97X-D/6-31G* equilibrium geometries for organic molecules. The model involves training to >6 million energy and force cal...
Journal of chemical information and modeling
39933880
Accurate prediction of molecular geometries is crucial for drug discovery and materials science. Existing fast conformer prediction algorithms often rely on approximate empirical energy functions, resulting in low accuracy. More accurate methods like...