An explainable multi-parameter optimization approach for de novo drug design against proteins from central nervous system

14 March 2022, Version 2
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

The aim of drug design and development is to produce a drug which can inhibit the target protein and possess a balanced physicochemical and toxicity profile. Traditionally, this is a multi-step process where different parameters such as activity, physicochemical and pharmacokinetic properties are optimized sequentially, which often leads to high attrition rate during later stages of drug design and development. We have developed a deep learning-based de novo drug design method which can design novel small molecules by optimizing target specificity as well as multiple parameters (including late-stage parameters) in a single step. All possible combinations of parameters were optimized to understand the effect of each parameter over the other parameters. An explainable predictive model was used to identify the molecular fragments responsible for the property being optimized. The proposed method was validated against the human 5-hydroxy tryptamine receptor 1B (5-HT1B), a protein from the central nervous system (CNS). Various physicochemical properties specific to CNS drugs were considered along with the target specificity and blood-brain barrier permeability (BBBP), which acts as an additional challenge for CNS drug delivery. The contribution of each parameter towards molecule design was identified by analyzing the properties of generated small molecules from optimization of all possible parameter combinations. The final optimized generative model was able to design both similar and better inhibitors compared to known inhibitors of 5-HT1B. In addition, the functional groups of the generated small molecules that guide the BBBP predictive model were identified through feature attribution techniques.

Keywords

Mutli-parameter optimization
Explainable models
Central nervous system
Blood-brain barrier

Supplementary materials

Title
Description
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Title
An explainable multi-parameter optimization approach for de novo drug design against proteins from central nervous system
Description
Contribution of properties considered during reinforcement learning
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