We introduce the QuanSA method for inducing physically meaningful field-based models of ligand binding pockets based on structure-activity data alone.
We introduce the ForceGen method for 3D structure generation and conformer elaboration of drug-like small molecules.
Surflex-QMOD integrates chemical structure and activity data to produce physically-realistic models for binding affinity prediction.
We present an approach that uses structural information known prior to a particular cutoff-date to make predictions on ligands whose bounds structures were determined later. The knowledge-guided docking protocol was tested on a set of ten protein targets using a total of 949 ligands.
Here we present an analysis of novel drug/target predictions, focusing on those that were not obvious based on known pharmacological crosstalk.
We present a hybrid structure-guided strategy that combines molecular similarity, docking, and multiple-instance learning such that information from protein structures can be used to inform models of structure–activity relationships.
This paper demonstrates how the Surflex-PSIM method can help investigate hypotheses around protein function in cases where function cannot be determined by sequence similarity.
Computational approaches for binding affinity prediction are most frequently demonstrated through cross-validation within a series of molecules or through performance shown on a blinded test set. Here, we show how such a system performs in an iterative, temporal lead optimization exercise. A series of gyrase inhibitors with known synthetic order formed the set of molecules that could be selected for “synthesis.”
This article discusses logic fallacies in the context of off-target predictive modelling.
To compare chemical structures, we can look at a number of 2D and 3D characteristics. In this paper, a group of 358 drugs with overlapping pharmacology were assessed for chemical similarity, using a new framework.
