Medicinal chemists are more often required to change molecular frameworks or scaffolds further by so-called core hopping to address scaffold-dependent issues. Artificial intelligence(AI) equips medicinal chemistry with innovative tools for small molecular design and lead discovery. AI-driven de novo drug design aims to generate new chemical entities with desired properties in a cost- and time-efficient manner. The ability of approaches provided by MedAI's AI platform to generate innovative molecular cores has been proved, thereby exploring novel regions of the chemical space.
The generative AI model has successfully produced molecules that possess features of the synthetic ChEMBL compounds used for model training. The model not only produced a high proportion of valid, stable and innovative structures but also captured the bioactivity of the templates.
Figure 1 Generative AI Model
Evolutionary algorithms are actively used for de novo drug design, which are based on those concepts derived from biological evolution, including reproduction, mutation (fragment-based mutation and atom-based mutation), crossover, and selection.
Building structures with chemical feasibility is an important point for de novo drug design. Generate chemical structures using fragment-based approaches and utilizing the structures of known ligands obey valence rules, RECAP-based rule (11 reaction schemes) or other connection rules.
Figure 2 De novo Drug Design
Developed computerized structural design approaches utilize protein-structures and/or ligand-structures as the structure-base design and ligand-based design, respectively. Site point connection method includes LUDI. Fragment connection methods include SPLICE, NEW LEAD, and PRO-LIGAND. Sequential build up methods include LEGEND, GROW, and SPROUT. Random connection and disconnection methods include CONCEPTS, CONCERTS, MCDNLG.
Figure 3 Available Programs for De novo Drug Design
Use target prediction method (SPiDER) and molecular shape and partial charge descriptors to determine the similarity of the designed compounds to known bioactive chemicals, taking into account their individual in silico ranks and building block availability.
Benchmark fingerprint descriptors for virtual screening to determine the structural similarity.
Confirm the designed compound that is not present in ChEMBL, PubChem, SureChEMBL, Reaxys and SciFinder databases.
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