LIGAND PROTEIN BINDING

I put together this resource for scientists navigating binding chemistry simulations to better map out their options before moving into experiments during early stage hit ID or lead optimization.

I've seen that different methods yield a milieu of results depending on how well an algorithm is able to model physically relevant biological systems, and results vary target to target. This is the framework I use when undertaking new or ongoing campaigns.

We lead decisions with understanding the objectives need to be executed experimentally, and we then strategically select the right algorithm based on operational constraints like compute, timing, accuracy of the models, and compound library size.

Overall, we need to segment hit discovery from lead optimization when selecting algorithms. In an ideal world we'd run sophisticated FEP for every compound, even for massive screens, but physically it's incomputable for massive libraries (that's why boltz2 was perceived as such a breakthrough - claiming FEP+ accuracy at ~1000x cost and time reductions, even if it struggles with generalizability).

Yes, we should analyze poses or binding affinities calculated by less sophisticated algorithms very carefully, but as we get closer to an experimental decision and the candidate library shrinks, you can introduce more thermodynamic variables and dynamics using more powerful models to get better simulations that resemble your biology.

If I begin with a 2M+ compound set, your campaign can look like this:

For Hit ID -

1) Quickly screen down using Static Docking (2M cmpds), Flexible Docking(40k), Ensemble Docking(3k) - slowly introducing more protein flexibility and dynamic behavior.

2) You can re-screen hits using protein-ligand MD and endpoint free energy MMPBSA/MMGBSA calculations(10-100)

3) Medicinal chemists review and send out biochemical experiments

For L/O -

1) Run QSAR on experimental results from previous step and your computational results, identify key moieties and scaffolds driving affinity

2) Run generative chemistry to get more creative designs of new molecules

3) Because these sets are smaller, you can utilize FEP (RBFE for congeneric series of ligands, Separated Topologies for non-congeneric ligand sets, and ABFE for high priority leads)

4) Synthesize using Revilico's cloud lab or CRO partners

5) Repeat designs, compute, experiment, cross-analyze data for correlations, repeat.

Computational models alone don't solve our problems, having the right strategy and benchmarks is even more important!

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