While accurately modeling the conformational ensemble is required for predicting properties
of flexible molecules, the optimal method of obtaining the conformational ensemble appears …

We assess the ability of our convolutional neural network (CNN)-based scoring functions to
perform several common tasks in the domain of drug discovery. These include correctly …

Small organic molecules are often flexible, ie, they can adopt a variety of low-energy
conformations in solution that exist in equilibrium with each other. Two main search …

The identification and optimization of promising lead molecules is essential for drug
discovery. Recently, artificial intelligence (AI) based generative methods provided …

We present a diffusion-based generative model for conformer generation. Our model is
focused on the reproduction of the bonded structure and is constructed from the associated …

Virtual screening is an essential part of the modern drug design pipeline, which significantly
accelerates the discovery of new drug candidates. Structure-based virtual screening …

While the use of deep learning in drug discovery is gaining increasing attention, the lack of
methods to compute reliable errors in prediction for Neural Networks prevents their …

Data-driven deep learning algorithms provide accurate prediction of high-level quantum-
chemical molecular properties. However, their inputs must be constrained to the same …

We assess and compare the performance of eight commercial conformer ensemble
generators (ConfGen, ConfGenX, cxcalc, iCon, MOE LowModeMD, MOE Stochastic, MOE …

Computer-aided drug design methods such as docking, pharmacophore searching, 3D
database searching, and the creation of 3D-QSAR models need conformational ensembles …