Publications & Presentations

Two separately fitted comparator models over one roster spanning 34 protein families, from kinases and GPCRs to proteases, ion channels and the epigenetic readers, writers and erasers. The compound preference model holds one target fixed and ranks two compounds; the target preference model holds one compound fixed and ranks two targets, from different families or from one family, with each kind measured on its own held-out comparisons. Every answer is an ordering with a prediction strength from 0.5 to 1.0, and accuracy climbs with strength: 0.71 over 65,725 held-out compound preference comparisons, and for target preference 0.75 over 8,689 comparisons between two families and 0.78 over 32,738 within one family, rising to 0.92, 0.89 and 0.91 at strength 0.70 and above. Trained on ChEMBL 37 alone, so both sets of weights are freely downloadable, and either model can be extended with a user's own measurements on their own machine, including targets added from an amino acid sequence.

Given one molecule, which proteins does it bind? Docking it into every characterized binding site answers directly and takes 40.5 hours on twenty cores. Instead every co-crystal ligand in the Protein Data Bank is indexed by the three-dimensional pharmacophore fingerprint it presents, predicted from flat structure by PharmCast, together with the proteins it was solved against. A query is fingerprinted in 4 milliseconds and searched against all 27,797 indexed ligands, covering 28,579 target sites, in 40 milliseconds, and only the proteins its nearest neighbors were crystallized with are docked. Across 3,000 held-out molecules, pooling the five most similar indexed ligands gives 21.1 candidate proteins and contains the known target 48.8 percent of the time at 108 seconds of docking; pooling twenty-five gives 104.5 proteins and 60.8 percent. Run cold, orforglipron returned the GLP-1 receptor ranked first and daraxonrasib returned its KRAS and cyclophilin A tri-complex fourth.

In the reference pipeline, generating 100 conformers requires 2.82 s of the 2.86 s needed to fingerprint one catalog compound; the bit calculation requires 0.039 s. We therefore removed the conformational stage. PharmCast is a feedforward neural network that predicts all 10,549 bits of a PharmPrint ensemble fingerprint directly from a SMILES string, comparing two molecules in 0.584 ms where the conventional pipeline takes 5.71 s. Version 10 was trained on 5,887,229 molecules from a screening collection, activity-backed ChEMBL compounds and peptide loops excised from crystal structures. Evaluated on 155,648 purchasable screening collection compounds excluded from every training set, 139,700 held-out ChEMBL compounds and 13,500 reserved peptide loops, median fingerprint error, Pearson r and pairwise ranking accuracy are 0.008, 0.980 and 0.936 for screening collection chemistry; 0.016, 0.984 and 0.952 for loop peptides; and 0.027, 0.936 and 0.889 for activity-backed ChEMBL compounds, against the reference calculation's own reproducibility of 0.006 and 0.995.

The consortium method: a compound's toxicity is predicted from the measured compounds it structurally resembles. Rebuilt in 2026 on the public record as ToxPred, it lands within one log unit of the measured rat oral LD50 92.1 percent of the time on the 1,336 of 2,873 held-out compounds it answered for, against 94 percent reported here.