
Alwin has over 7 years of experience applying ML to drug discovery, including 3.5 years focused on method development and application of co-folding models in discovery programs at CHARM Therapeutics. At Apheris, he is working on advancing co-folding model architectures and training strategies across distributed datasets to maximize AI model improvements.

We fine-tuned OpenFold3 on just 10 PDE10A protein–ligand complexes and evaluated on 17 held-out structures. Even this low-n setup corrected systematic pose errors and improved interface metrics, making predictions more usable for design decisions.

We tested how well OpenFold3 predicts a novel protein–ligand complex by reproducing a SIK3–inhibitor complex and analyzing its selectivity over AMPK. Using ApherisFold, we accurately replicated the experimental ligand pose and could rationalize the observed selectivity through steric effects.
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