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Data science has emerged as an innovative tool in the biopharmaceutical industry, leveraging the power of machine learning and artificial intelligence to drive innovation and efficiency across the entire drug development lifecycle.
“AI will not replace drug discovery scientists, but drug discovery scientists who use AI will replace those who don’t” – comment during EFMC meeting 2018 Progressing a drug molecule from concept to commercialisation typically takes 10-15 years and has high associated costs of up to $2 billion per launched drug, if all failures are factored in.
Previous Next In an earlier post I considered what it might mean to describe drug design as AI-based. In that earlier post I noted that there’s a bit more to drug design than simply generating novel molecular structures and suggesting how the compounds should be synthesized.
TEAD proteins are crucial for tumour progression and drug resistance, making them an attractive focus for therapeutic interventions. Leveraging AI-guided structure-based drug design, Insilico’s research and development team generated an impressive portfolio of over 6,000 molecules and identified three highly promising hit series.
Most papers describing new methods for machine learning (ML) in drug discovery report some sort of benchmark comparing their algorithm and/or molecular representation with the current state of the art. Most pharmaceutical compounds tend to have solubilities somewhere between 1 and 500 µM. We want to design drugs that are safe.
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. Even if they did, they needed to articulate the implications for drug design a lot more clearly than they have done.
2 Figure 1 Not only does the carboxylic acid moiety of the infamous drug zomepirac undergo conjugation to an unstable acyl glucuronide, the pyrrole undergoes oxidative metabolism to an epoxide intermediate that can be trapped by glutathione (Figure 2). 2a Figure 2 Also reported are more complex “per-oxidative” oxidations.
Review articles 2023 was a bit of a mixed bag for AI in drug discovery. Benchmarking Generated Poses: How Rational is Structure-based Drug Design with Generative Models [link] 1.4 How accurately can one predict drug binding modes using AlphaFold models? Docking, protein structure prediction, and benchmarking II.
Breaking C-F bonds in drugs – metabolism mediated release of fluorine By Samuel Coe and Julia Shanu-Wilson Lenacapavir, recently approved for multi-drug resistant HIV-1 infection, contains 10 fluorine atoms. Increasingly used in drug design, some drug structures are now bristling with fluorine atoms.
What are macrocycles and why are they interesting for drug discovery? Traditionally, drug discovery has focused on small-molecule therapeutics, typically with a molecular weight of less than 500 Daltons. 2 Typically, small-molecule drugs target active sites buried inside proteins. 8 Why are there not more macrocyclic drugs?
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