All tools

Predict ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties from SMILES strings using machine learning models trained on Therapeutics Data Commons datasets.

Predict 22 ADMET properties from SMILES strings with the native Admetica Chemprop models from Datagrok.

AF2BIND predicts ligand-binding residues from a protein structure using AlphaFold2 pair representations and a 20-residue bait sequence.

Identify toxic, reactive, and pharmacokinetically problematic molecular fragments using structural alert patterns

DLKcat predicts enzyme turnover numbers (kcat values) from protein sequences and substrate structures using its published deep-learning model.

Predict toxicity and synthetic accessibility of small molecules using machine learning. eToxPred combines toxicity risk assessment with synthetic accessibility scoring to help prioritize drug candidates.

Open-source protein pocket detection using Voronoi tessellation and alpha spheres. Identifies ligand binding sites with druggability scores.

Screen for lead-like compounds using stricter molecular descriptor criteria than Lipinski or Veber rules for early-stage drug discovery

Lipinski's Rule of Five predicts whether compounds will be orally bioavailable by evaluating molecular weight, LogP, hydrogen bond donors, and acceptors.

Predict protein solubility and usability for E. coli expression using ESM protein language models

Screen compounds for Pan-Assay Interference patterns that cause false positives in biological assays

Quantitative estimate for protein-protein interaction inhibitor potential. Evaluates drug-likeness for compounds targeting PPIs.

Deep learning framework for predicting small molecule-RNA interactions using RNA secondary structure. Combines language models, CNNs, and graph attention networks for binding prediction.

Rank a compound library against one protein target with SPRINT protein and ligand co-embeddings and native cosine similarity.

Screen compounds for structural toxicity alerts using PAINS, Brenk, and NIH filters. For focused screening, see PAINS Filter, Brenk Filter, or Veber's Rule.

Screen molecular flexibility and polarity using Veber's rotatable-bond and topological polar surface area criteria.