Quantitative surface field analysis: learning causal models to predict ligand binding affinity and pose.

Abstract:

:We introduce the QuanSA method for inducing physically meaningful field-based models of ligand binding pockets based on structure-activity data alone. The method is closely related to the QMOD approach, substituting a learned scoring field for a pocket constructed of molecular fragments. The problem of mutual ligand alignment is addressed in a general way, and optimal model parameters and ligand poses are identified through multiple-instance machine learning. We provide algorithmic details along with performance results on sixteen structure-activity data sets covering many pharmaceutically relevant targets. In particular, we show how models initially induced from small data sets can extrapolatively identify potent new ligands with novel underlying scaffolds with very high specificity. Further, we show that combining predictions from QuanSA models with those from physics-based simulation approaches is synergistic. QuanSA predictions yield binding affinities, explicit estimates of ligand strain, associated ligand pose families, and estimates of structural novelty and confidence. The method is applicable for fine-grained lead optimization as well as potent new lead identification.

journal_name

J Comput Aided Mol Des

authors

Cleves AE,Jain AN

doi

10.1007/s10822-018-0126-x

subject

Has Abstract

pub_date

2018-07-01 00:00:00

pages

731-757

issue

7

eissn

0920-654X

issn

1573-4951

pii

10.1007/s10822-018-0126-x

journal_volume

32

pub_type

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