Advances in Computational Drug Design and Natural Therapeutics: Integrating Artificial Intelligence, Molecular Modeling, and Precision Drug Discovery

Natural compounds offer a large array of molecules having promising therapeutic properties, although the discoveries of such molecules are restricted by the chemical complexity, rarity, multiple rediscoveries, insufficient target identification and experimental screening efforts. This literature review summarizes the current state of knowledge about AI-driven natural-product discovery, drug-designing computations, molecular structure prediction, multi-omics analysis, ADMET evaluation and precision medicine. The main focus of this review was made on the quantitative information available in peer-reviewed studies and on the correlation between computational predictions and experimental data. The published data shows the persistent role of natural-product chemistry in drug discovery, with the direct contribution of natural-product molecules to small-molecule anticancer drugs reaching up to one-third, and indirect – two-thirds. The performance of an existing AlphaFold-assisted proteo-chemometric model that was able to predict the interactions correctly in 73% of unseen targets has been reported, although there is an even better performance recorded in a smaller percentage of such cases. There are models based on the multi-omics approach that have been shown to perform very well in predicting the response to drugs, while the performance of AI-based ADMET models is also quite promising. All of the above is an indication that a closed loop-based approach can be developed including the following components: natural source characterization, prioritization using AI, modeling, disease matching, and validation.