blog · category
8 articles on drug discovery, newest first.
the log
8 articles · Drug Discovery
all articleslatest article · Drug Discovery
DiffDock finds where a ligand binds on a whole protein; GNINA and AutoDock Vina are strongest when you already know the pocket. Here is how the published benchmarks compare, why pose accuracy is not physical validity, and why no docking score is a binding affinity.
· 8 min read
An in silico ADMET panel ranks compounds; in vitro assays measure them and in vivo studies measure the animal. Here is what each layer actually resolves, where predictions fail, and how to decide when a computational flag justifies an experiment.
A DeepPurpose drug–target interaction score is a sequence-only estimate of the label its checkpoint was trained on: pKd on a kinase panel, pIC50 from BindingDB, or a KIBA score. A binding assay measures the interaction itself. What each number is, how far apart they can be, and how to use the score without mistaking it for a Kd.
Docking places a ligand into a receptor structure you already have. Co-folding predicts the whole complex from sequence. Here is what DiffDock, Chai-1 and Boltz-2 each need as input, what their confidence scores mean, what Boltz-2's affinity head is and is not, and when to reach for each.
GenMol samples molecules as fragments with masked discrete diffusion; REINVENT 4 steers an RNN or transformer prior with reinforcement learning and curriculum scoring. Here is what each one is built for, what GuacaMol and MOSES do and do not measure, and how to run them.
ADMET-AI, SwissADME and pkCSM are all free, and they are not the same kind of instrument. One returns learned probabilities and regression values at library scale, one returns rules and physicochemical readouts, one returns regression and classification from graph-based signatures. Here is how to pick.
RDKit's MolLogP is the Wildman–Crippen atom-contribution model, and TPSA is a fragment sum that stands in for 3D polar surface area. Both are fast, deterministic and good enough to rank and filter compounds. Neither is a measurement. A practical comparison of calculated logP, TPSA and the rule-of-five descriptors against shake-flask logP and measured aqueous solubility, with the published error bars.
Learn how to predict absorption, distribution, metabolism, excretion, and toxicity properties of drug candidates using AI models — before spending resources on synthesis.
newsletter
