Understanding Drug Resistance
in Oncology
OncoResist is a curated database and AI-powered research tool for exploring how cancer cells develop resistance to FDA-approved targeted therapies. Built for researchers, clinicians, and students who need fast, reliable answers grounded in real data.
Drug Resistance is the Central Challenge in Cancer Treatment
Targeted therapies have transformed oncology — drugs like imatinib, erlotinib, and vemurafenib produce remarkable initial responses. But cancer cells adapt. Through acquired mutations in drug targets, activation of bypass pathways, and off-target genomic alterations, tumors develop resistance and treatment fails.
Understanding which mutations cause resistance, which drugs share resistance profiles, and what the clinical literature says about specific alterations requires navigating multiple disconnected databases — COSMIC, DrugBank, UniProt, PubMed — and synthesizing information manually. OncoResist brings this together in one place.
Built for Research, Not Just Browsing
Browse resistance mutations for any FDA-approved oncology drug, classified as on-target or off-target from COSMIC Drug Resistance data. See which genes are implicated and how many patient samples carry each mutation.
Click any mutation chip to highlight the mutated residue directly on the protein's 3D structure. Structures are fetched live from the RCSB Protein Data Bank with SIFTS residue mapping for accurate positioning.
Select resistance mutations and download a computationally generated mutant PDB file for use in PyMOL, UCSF Chimera, or other structural biology tools. No registration required.
Discover which drugs share protein targets, which mutations confer pan-resistance across multiple therapies, and how resistance profiles differ between drug generations — without manually cross-referencing multiple databases.
Compare physicochemical and drug-likeness properties of any two drugs side by side. Molecular weight, logP, TPSA, hydrogen bond donors and acceptors, Lipinski and Veber rule compliance — computed directly from SMILES structures.
Ask natural language questions and get answers grounded in the database — not hallucinated. The assistant cites specific PubMed references, links to portal pages, and reasons across multiple data sources to answer complex queries.
How Researchers Use OncoResist
Navigate to the drug page, review on-target mutations grouped by gene, click chips to see where mutations fall on the 3D structure, and download mutant coordinates for further docking or MD simulation.
Ask the AI assistant about a mutation like T315I — it will identify all drugs affected, retrieve clinical evidence from indexed PubMed papers, and explain the structural basis of resistance with inline citations.
Use the resistance landscape tool to see how resistance has evolved across drug generations for a target like ABL1 or EGFR — from first-generation inhibitors through to drugs specifically designed to overcome resistance.
Compare ADMET profiles of structurally similar drugs to understand how physicochemical properties differ and what trade-offs exist between potency and oral bioavailability.
Where the Data Comes From
OncoResist integrates data from four primary sources, processed and linked into a unified SQLite database.