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cc: Life Science - Accelerating Drug Discovery with Virtual Screening

Tom talks about AI and Virtual screening for Drug Discovery

Summary from cc: Life Science Podcast page

Tom Pesnot is the Head of Medicinal Chemistry at Concept Life Sciences. I invited him to talk about AI and virtual screening in the drug discovery process.

By way of review, Tom laid out the overall process of discovery. One needs to identify a target whose activity can be modulated in a way that is of course, relevant to the disease of interest. Most often we are trying to stop a protein from carrying out its normal function.

Then we are looking for hits — interactions of candidate compounds with the target molecule. The quality of those hits are important.

Typically, this has been done in high throughput screening using in vitro assays. This requires lots of compounds and lots of assays, making the process inaccessible for many. As you might imagine, it is very expensive with fancy robots etc.

All of this provides the rationale for virtual screening because computers are becoming more powerful for predicting interactions between small molecule compounds and target proteins.

Instead of starting with a compound collection (that few have access to), you start with a database. It’s possible to virtually make tens of billions of compounds in silico for screening. What blew my mind was the fact that they are only screening molecules that can be made in two or three steps from existing building blocks. Tens of billions! That means the time from identification to testing is essentially the time needed for shipping the constituent compounds.

And of course, at the other end, you still need a model to recapitulate the proposed activity in vitro.

AI is used along with known protein structures to see what molecules fit and how well in the target’s binding site. I asked about binding in other places that would affect activity. Ligand-based interactions are legitimate, Tom told me. For example, GPCRs (G-protein coupled receptors) elicit different pharmacology depending on where binding occurs, but AI has more impact in structure-based screening focused on active site binding.

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