Abstract
The novel approaches of q-RA and q-RASAR appear to have much promise in quantitative predictions and data gap-filling with applications in drug design, materials science, and predictive toxicology. The similarity metrics and error considerations may be further refined, possibly with the application of sophistical machine learning approaches, for further development of this new field. More extensive applications of q-RA and q-RASAR in medicinal chemistry research may be rewarding for lead optimization and pharmacokinetic fine-tuning. These new approaches may also be applied in several other diverse fields like agriculture, food, nanosciences, etc.
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Roy, K., Banerjee, A. (2024). Future Prospects. In: q-RASAR. SpringerBriefs in Molecular Science. Springer, Cham. https://doi.org/10.1007/978-3-031-52057-0_5
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DOI: https://doi.org/10.1007/978-3-031-52057-0_5
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