Computational discovery of small bioactive molecules with molecular docking
Molecular docking is the most practical approach to use protein structures to find new small molecules for biological applications. Notwithstanding recent successes, the method retains important liabilities that make it challenging to deploy on a large scale. We have therefore created an expert system, DOCK Blaster, to investigate the feasibility of complete automation1. DOCK Blaster requires a Protein DataBank (PDB) code, sometimes with a ligand structure, and from that alone can launch a full screen of large chemical libraries. A critical feature is self-assessment, since the method rapidly produces far more results than can be inspected manually. We use the ChEMBL medicinal chemistry database to estimate the anticipated reliability of the automated screening results by the ability to recapitulate known binding data from the literature. From over 4000 proteins tested so far, over half yield results that look compelling enough to commit time and money to pursuing, which we are now doing.
John Irwin is Adjunct Associate Professor of Pharmaceutical Chemistry at the University of California San Francisco. For the past decade he has been working on computational ligand discovery, first at Northwestern University Medical School and since 2003 at UCSF. He creates and curates software tools and databases for ligand discovery including:
* ZINC, a free database of commercially available compounds for virtual screening
* DOCK Blaster, a free target based virtual screening web service
* DUD, a dataset for benchmarking target-based virtual screening methods and
* SEA, the Similarity Ensemble Approach for predicting biological targets for molecules.