LAIgnd: Revolutionizing Drug Discovery with Advanced AI-Driven Molecule Generation
E.D. Obi, J.A. Yentumi, D. Mbatuegwu, Olaposi Idowu Omotuyi, O.O. Ajayi, A. Nwokoro · Advances in Multidisciplinary & Scientific Research Journal Publication · 2024
De novo molecular generation is crucial for advancing drug discovery and chemical research. This accelerates the search for new drug candidates and deepens our understanding of molecular diversity. The development of deep learning has propelled and expedited the de novo molecular generation. Generative networks, particularly Variational Autoencoders (VAEs), can randomly produce new molecules and modify molecular structures to enhance specific chemical properties, which are essential for advancing drug discovery. Although VAEs offer numerous advantages, they are hindered by limitations that affect their capacity to optimize properties and decode syntactically valid molecules. To address these challenges, we present LAIgnd, a de novo drug molecule generation model that implements a custom β-CVAE architecture conditioned on protein sequences and SELFIES input. Extensive experiments have shown that LAIgnd generates a wide variety of valid, novel, and effective molecules for complex and simple diseases, demonstrating its robustness and generalization capabilities. Additionally, by employing molecular docking, toxicity, similarity, and synthetic accessibility experiments, we demonstrated the drug-likeness and effectiveness of the generated molecules. The ability of our model to generate novel and diverse compounds was illustrated by a case study focusing on Lung Cancer. A total of four hundred (400) molecules were generated by LAIgnd, with a high number of molecules exhibiting strong inhibitory activity against the Epidermal Growth Factor receptor, as indicated by binding affinities. LAIgnd provides new insights into future directions to enhance therapeutics for complex and simple diseases by generating high-quality multi-target molecules for drug discovery. Key words: De novo molecular generation, Drug discovery, Variational Autoencoders (VAEs), SELFIES, Protein sequences. CISDI Journal Reference Format Obi, E.D., Yentumi, J.A., Mbatuegwu, D., Omotuyi, O.I., Ajayi, O.O. & Nwokoro, A. (2024): LAIgnd: Revolutionizing Drug Discovery with Advanced AI-Driven Molecule Generation. Computing, Information Systems, Development Informatics & Allied Research Journal. Vol 15 No 4, Pp 1-10. Available online at www.isteams.net/cisdijournal. dx.doi.org/10.22624/AIMS/CISDI/V15N3P4