Abstract
Cheminformatics utilizing machine learning (ML) techniques have opened up a new horizon in drug discovery. This is owing to vast chemical space expansion with rocketing numbers of expected hits and lead compounds that match druggable macromolecular targets, in particular from natural compounds. Due to the natural products’ (NP) structural complexity, uniqueness, and diversity, they could occupy a bigger space in pharmaceuticals, allowing the industry to pursue more selective leads in the nanomolar range of binding affinity. ML is an essential part of each step of the drug design pipeline, such as target prediction, compound library preparation, and lead optimization. Notably, molecular mechanic and dynamic simulations, induced docking, and free energy perturbations are essential in predicting best binding poses, binding free energy values, and molecular mechanics force fields. Those applications have leveraged from artificial intelligence (AI), which decreases the computational costs required for such costly simulations. This seminar aimed to describe chemical space and compound libraries related to NPs. Highthroughput virtual screening and their strategies in leveraging NPs libraries can be optimized to match the specificity of the chemical space that is occupied by such kind of complex compounds. Particular emphasis was given to AI approaches, ML tools, algorithms, and techniques, especially in drug discovery of macrocyclic compounds and approaches in computer-aided and ML-based drug discovery. The various functionalities and stereochemical complexities of macrocycles give them more selectivity and affinity to protein targets. Natural products were discussed as having the most distinct features differentiating them from synthetic compounds by the number of aromatic atoms, chiral centers, nitrogen, and oxygen atoms. Aromaticity is eminent among the synthetic compounds, while the chiral centers are more prevalent in NP compounds. Furthermore, the oxygen atoms are more prevalent in NPs, while nitrogen atoms are less. Those features make NPs as source of new lead compounds that can be developed using ML tools for diverse medicinal uses specifically in cancer, infectious diseases, and metabolic disorders.
References
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Details
| Primary Language | English |
|---|---|
| Subjects | Pharmacology and Pharmaceutical Sciences (Other) |
| Journal Section | Reviews |
| Authors | |
| Publication Date | June 28, 2025 |
| Published in Issue | Year 2023 Volume: 27 Issue: Current Research Topıcs In Pharmacy: Pharmacology Debates |