Combining the Power of Attention Models and Many-objective Computational Intelligence Algorithms for Drug Design

AI-based approaches have been recently applied to in silico drug design. However, existing approaches and protocols consider the absorption, distribution, metabolism, excretion, and toxicity (ADMET) pharmacokinetic properties of drug candidates in a later stage of drug design processes, where failure is most costly. To address this challenge, this research work aims to achieve three objectives. First, it explores the use of Transformer-based models for ADMET prediction based on a hybrid fragment-SMILES tokenization scheme and two training strategies. Second, it evaluates the performance of contrastive Transformer-based latent models for molecular generation. Third, it applies many-objective computational intelligence algorithms in the continuous latent space generated by a Transformer model to generate optimal drug candidates that fulfill ADMET and other essential properties in parallel. The results of this research work demonstrate superiority in the hybrid approach over SMILES in predicting ADMET properties. Furthermore, the system proposed in this study integrates metaheuristics with ADMET prediction and latent Transformer models for solving a drug design problem. A comparative study shows effectiveness of computational intelligence towards a many-objective drug design problem, where 1718 drug-like molecules are obtained after application of a strict filtering criteria.

Name thesis:degree_name

M.Sc. Computer Science

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Faculty of Mathematics and Science

Department dc:contributor.department

Department of Computer Science

Grantor

Brock University

Year dc:date.issued

2024

Author dc:creator

• Aksamit, Nicholas

• cancer drug design
• Transformers
• ADMET prediction
• molecular generation
• many-objective metaheuristics

Statement dc:rights

• CC0 1.0 Universal

Licence dc:rights.uri

• http://creativecommons.org/publicdomain/zero/1.0/

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/10464/18266

OAI identifier oai:identifier

oai:brocku.scholaris.ca:10464/18266