DeLA-DrugSelf: Empowering multi-objective de novo design through SELFIES molecular representation

• Published in: Computers in biology and medicine 2024-06, Vol.175, p.108486, Article 108486
• Main Authors: Alberga, Domenico, Lamanna, Giuseppe, Graziano, Giovanni, Delre, Pietro, Lomuscio, Maria Cristina, Corriero, Nicola, Ligresti, Alessia, Siliqi, Dritan, Saviano, Michele, Contino, Marialessandra, Stefanachi, Angela, Mangiatordi, Giuseppe Felice
• Format: Article
• Language: English
• Subjects: Algorithms; Cannabinoid CB2 receptors; Deep learning; Drug Design; Empowerment; Genetic algorithms; Humans; Multiple objective analysis; Mutation; Neural networks; Pareto optimization; Probability distribution; Proteins; Representations; Software; Strings; Supercomputers
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.108486

In this paper, we introduce DeLA-DrugSelf, an upgraded version of DeLA-Drug [J. Chem. Inf. Model. 62 (2022) 1411–1424], which incorporates essential advancements for automated multi-objective de novo design. Unlike its predecessor, which relies on SMILES notation for molecular representation, DeLA-DrugSelf employs a novel and robust molecular representation string named SELFIES (SELF-referencing Embedded String). The generation process in DeLA-DrugSelf not only involves substitutions to the initial string representing the starting query molecule but also incorporates insertions and deletions. This enhancement makes DeLA-DrugSelf significantly more adept at executing data-driven scaffold decoration and lead optimization strategies. Remarkably, DeLA-DrugSelf explicitly addresses the SELFIES-related collapse issue, considering only collapse-free compounds during generation. These compounds undergo a rigorous quality metrics evaluation, highlighting substantial advancements in terms of drug-likeness, uniqueness, and novelty compared to the molecules generated by the previous version of the algorithm. To evaluate the potential of DeLA-DrugSelf as a mutational operator within a genetic algorithm framework for multi-objective optimization, we employed a fitness function based on Pareto dominance. Our objectives focused on target-oriented properties aimed at optimizing known cannabinoid receptor 2 (CB2R) ligands. The results obtained indicate that DeLA-DrugSelf, available as a user-friendly web platform (https://www.ba.ic.cnr.it/softwareic/delaself/), can effectively contribute to the data-driven optimization of starting bioactive molecules based on user-defined parameters. •We developed DeLA-DrugSelf, a generative algorithm for multi-objective de-novo design.•DeLA-DrugSelf employs a robust molecular representation string named SELFIES.•The generation process involves not only substitutions but also insertions and deletions.•DeLA-DrugSelf is available as a user-friendly web platform.