Deep Generative Models for Ligand-based de Novo Design Applied to Multi-parametric Optimization

Abstract

<div> <div> <div> <p>Multi-Parameter Optimization (MPO) is a major challenge in New Chemical Entity (NCE) drug discovery projects, and the inability to identify molecules meeting all the criteria of lead optimization (LO) is an important cause of NCE project failure. Several ligand- and structure-based de novo design methods have been published over the past decades, some of which have proved useful multiobjective optimization. However, there is still need for improvement to better address the chemical feasibility of generated compounds as well as increasing the explored chemical space while tackling the MPO challenge. Recently, promising results have been reported for deep learning generative models applied to de novo molecular design, but until now, to our knowledge, no report has been made of the value of this new technology for addressing MPO in an actual drug discovery project. Our objective in this study was to evaluate the potential of a ligand-based de novo design technology using deep learning generative models to accelerate the discovery of an optimized lead compound meeting all in vitro late stage LO criteria. </p> </div> </div> </div>

Target-Based Evaluation of “Drug-Like” Properties and Ligand Efficiencies

“…Overcoming any hurdles preventing close collaboration with computational chemists will be essential for success in these areas. , An example, said to be the first report of using a deep learning generative model in multiparameter lead optimization, is the discovery of 7 from the previous best compound 6 (Figure ). Compound 7 was one of only 11 compounds synthesized from a total of 150 predicted to have better overall properties than 6 . The triazine moiety in 7 had been tried earlier, and it performed poorly until the pyridyl amide modification was suggested by the generative tool.…”

“…Multiparameter lead optimization using generative predictive tools. Compounds 6 and 7 act as activators of an undisclosed phenotypic target . Compound 6 was the best of 881 compounds across 6 off-target and 4 ADME assays.…”

Yin-yang in drug discovery: rethinking de novo design and development of predictive models

“…Deep learning, currently used in ligand-based de novo design, learns the probability distribution of molecular data and generates continuous or discrete latent representations for molecules with property optimization (Gómez-Bombarelli et al, 2018). The algorithms map the learned probability distribution and molecule representation into novel molecules while optimizing molecular properties (Bilodeau et al, 2022) through the tuning of hyperparameters (Perron et al, 2022a;Bender et al, 2022). Advances in deep learning are significantly advancing molecule generation, representing a big step forward in bridging the gap between chemical entities and drug-like properties (Krishnan et al, 2021).…”