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Assessment of AI-generated chemical structures using ML

Molecular Design

One way of doing this is to build models for predicting biological activity and other pharmaceutically relevant properties such as aqueous solubility, permeability and metabolic stability. Generally you should also validate your models and this is especially important for models with large numbers of adjustable parameters.

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How the AI revolution can accelerate early drug discovery

Drug Target Review

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.

Drugs 105
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Efficient trajectories

Molecular Design

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.

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AI in Drug Discovery 2023 - A Highly Opinionated Literature Review (Part I)

Practical Cheminformatics

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.

Drugs 143