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The Data-Driven Future of Drug Development

DrugBank

By harnessing the vast amounts of data generated throughout the development pipeline, pharmaceutical companies can accelerate the discovery of novel therapies, optimize clinical trial design, enhance drug safety monitoring, and deliver personalized medicine, ultimately improving patient outcomes and transforming the future of healthcare.

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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. Research and Development in the Pharmaceutical Industry | Congressional Budget Office [Internet].

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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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Game-changing pan-TEAD inhibitor for solid tumours

Drug Target Review

The system optimised the molecules that were most likely to succeed in terms of potency, metabolic stability, synthetic accessibility, and more. The novel molecules were further ranked based on their ADME and selectivity profiles. He is an adjunct professor of artificial intelligence at the Buck Institute for Research on Aging.

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Efficient trajectories

Molecular Design

While the Ro5 article highlighted molecular size and lipophilicity as pharmaceutical risk factors, the rule itself is actually of limited utility as a drug design tool. The orchids in Blanchisseuse have been particularly good this year and I’ll include some photos of them to break the text up a bit.

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We Need Better Benchmarks for Machine Learning in Drug Discovery

Practical Cheminformatics

Most pharmaceutical compounds tend to have solubilities somewhere between 1 and 500 µM. Over the last 40 years, the pharmaceutical industry has developed a wide range of invitro and invivo assays for assessing the safety of drug candidates. This is not the case for the ESOL aqueous solubility dataset in MoleculeNet.

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Metabolism of five membered nitrogen containing heterocycles

Metabolite Tales Blog

2018, 26, 965-972 [link] 4a Pharmaceutics, 2022, 14, 1001 [link] 5 Phys. 2017, 30, 1, 13–37 [link] 2a Drug Metab. 2006 , 34, 145-51 [link] 3 Drug Metab. 1980, 8, 34-38 [link] 4 J. Food Drug Anal. 2000, 2, 195-201 [link] 6 Biochem. 1966 98, 266-277 [link] 7 Metabolites. 2023, 13, 1, 92- [link] 8 J. 2018, 61, 5, 2041–205 [link] 9 ACS Chem.