Automated design of ligands to polypharmacological profiles

By J. Besnard, G. F. Ruda, V. Setola, Keren Abecassis, R. Rodriguiz, Xi-Ping Huang, S. Norval, M. F. Sassano, Antony I. Shin, Lauren A. Webster and 11 more. 2012

781 citations recorded by the source corpus.

The clinical efficacy and safety of a drug is determined by its activity profile across many proteins in the proteome. However, designing drugs with a specific multi-target profile is both complex and difficult. Therefore methods to design drugs rationally a priori against profiles of several proteins would have immense value in drug discovery. Here we describe a new approach for the automated design of ligands against profiles of multiple drug targets. The method is demonstrated by the evolution of an approved acetylcholinesterase inhibitor drug into brain-penetrable ligands with either specific polypharmacology or exquisite selectivity profiles for G-protein-coupled receptors. Overall, 800 ligand–target predictions of prospectively designed ligands were tested experimentally, of which 75% were confirmed to be correct. We also demonstrate target engagement in vivo. The approach can be a useful source of drug leads when multi-target profiles are required to achieve either selectivity over other drug targets or a desired polypharmacology.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

Junction Tree Variational Autoencoder for Molecular Graph Generation (2018, background): “This is a more realistic scenario in drug discovery, where development of new drugs usually starts with known molecules such as existing drugs (Besnard et al., 2012).”
Molecular de-novo design through deep reinforcement learning (2017, background): “For example, Besnard et al. [7] applied a transformation rule approach to the design of novel dopamine receptor type 2 (DRD2) receptor active compounds with speciﬁc polypharma-cological proﬁles and appropriate DMPK proﬁles for a central nervous system indication.”
The ChEMBL bioactivity database: an update (2013, background): “…target; investigation of the selectivity and off-targets effects of drugs; large-scale data mining, such as the construction of predictive models for targets and identiﬁcation of bioisostere replacements or activity cliffs (1–4); and as a key component of integrated drug discovery platforms (5–7).”
What Is High-Throughput Virtual Screening? A Perspective from Organic Materials Discovery (2015, neutral): “(21, 22, 23, 24, 25, 26, 27, 28, 29, 30).”
5-HT2C Receptor Structures Reveal the Structural Basis of GPCR Polypharmacology (2018, methodology): “edu/pdspWeb/ for the National Institute of Mental Health Psychoactive Drug Screening Program (NIMH PDSP) (Besnard et al., 2012).”
From evolutionary computation to the evolution of things (2015, methodology): “For instance, Besnard et al. mined the ChEMBL database to discover a set of transformations of chemical structures that they then used as the mutation operator in an automated drug design application 29 .”
A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality (2017, background): “…the presence of microbiota in the human gut [75] and termites [76], protein analysis and genetics [77]; biotechnology with the design of new drugs [78,79]; artificial intelligence with robots adapting like animals [80] and programs mastering the GO game [81]; psychology by profiling…”