Abstract
Computer simulations to address research problems have touched almost all the sectors of science. Ethnopharmacological data demands linkage with different biomedical databases for both prospective and retrospective studies. Computational intelligence bridges the gap between diverse resources already available (from the molecular level to macroscopic characteristics of drugs) marking its significance in computer-aided drug design and gives an insight into the mechanism of action of bioactive compounds. Computational methods are utilized to study the process of extraction, isolation, structure prediction, metabolomics, biosynthesis, dereplication, phytochemical library construction, and assessment of their bioactivity. Further, in silico models aim to minimize the cost and time associated with conventional screening techniques. Electronic structure determination, docking, geometry optimization are few methods to name that are popular in biological sciences. Softwares like Gaussian 94, MOPAC, GAMES, Spartan, and Sybyl showed their dominant role in deciphering the chemistry underlying a process. The complexity of collecting vast biological data and extracting the information of interest for subsequent knowledge provision and deduction of conclusions is mollified to a great extent using bioinformatics. This review discusses the hurdles, current progress, and potential of computational intelligence in drug discoveries/interactions.
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Adhikary, T., Basak, P. (2020). Interdisciplinary Approaches Incorporating Computational Intelligence in Modern Pharmacognosy to Address Biological Problems. In: Mallick, P.K., Meher, P., Majumder, A., Das, S.K. (eds) Electronic Systems and Intelligent Computing. Lecture Notes in Electrical Engineering, vol 686. Springer, Singapore. https://doi.org/10.1007/978-981-15-7031-5_2
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