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

revised on 22.01.2021, 10:10 and posted on 25.01.2021, 08:03 by Quentin Perron, Olivier Mirguet, Hamza Tajmouati, Adam Skiredj, Anne Rojas, Arnaud Gohier, Pierre Ducrot, Marie-Pierre Bourguignon, Patricia Sansilvestri-Morel, Nicolas Do Huu, Françoise Gellibert, Yann Gaston-Mathé

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.


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Declaration of Conflict of Interest

Yann Gaston-Mathé, Nicolas Do-Huu and Quentin Perron are co-founders of Iktos ( and respectively CEO, CTO and CSO of the company. Hamza Tajmouati and Adam Skiredj are employees of Iktos. Olivier Mirguet, Anne Rojas, Arnaud Gohier, Pierre Ducrot, Marie-Pierre Bourguignon, Patricia Sansilvestri-Morel and Françoise Gellibert are all employees of Servier.

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