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One way of doing this is to build models for predicting biological activity and other pharmaceutically relevant properties such as aqueous solubility, permeability and metabolicstability. Generally you should also validate your models and this is especially important for models with large numbers of adjustable parameters.
Training AI/ML tools to predict results of otherwise complex and time-consuming calculations is gaining traction in pharmaceutical R&D. To really benefit from AI, the pharmaceutical industry must be more open to data sharing. He has published 22 scientific articles and has a h-index of 13. Matthew is a chartered chemist.
I'll examine an article entitled ‘Mapping the Efficiency and Physicochemical Trajectories of Successful Optimizations’ (YL2018) in this post and I should note that the article title reminded me that abseiling has been described as the second fastest way down the mountain.
Review articles 2023 was a bit of a mixed bag for AI in drug discovery. Prospective Validation of Machine Learning Algorithms for Absorption, Distribution, Metabolism, and Excretion Prediction: An Industrial Perspective [link] One of my favorite papers of 2023 provided a tour de force in method comparison.
Their collaborative work continues to drive advancements in computational methods, offering new solutions to the intricate problems of preclinical pharmaceutical discovery through innovative predictive modelling techniques. Pharmaceuticals , 16(7):996, 2023. Cyclic Peptide Design, Chapter 2: Strategies to Enhance MetabolicStabilities.
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