#11thICCS Steven Oatley on Active search for computer-aided drug design
The disease we are working on is Idiopathic pulmonary fibrosis (IPF) - progressive lung disease. Scar tissue in the lungs. Shortness of breath, dry cough. Often fatal within 2-5 years. Worse for smokers. Genetic factor.
Starts via micro injuries. Death of eopithelial cells, and then abnormal healing process leading to a cycle of repetitive lung remodelling.
Integrins. Large transmembrane signaling proteins. Responsible for regulation of cell cycle and imune responses (e.g. scar formation). Two strands, alpha and beta. av (alpha v) is the alpha subunit that is of interest. Also b3,6 (beta 3,6) of interest here.
Crystal structures available in combination with TGFbeta1 (ed: I think?). To prevent the natural ligand from binding, we use a RGD mimetic. Naphthyridine fragment as arginine mimic.
185K cmpds to test. Activie learning using an adaptive Markov chain based approach. We start by generating 10 cmpds and sent to docking program (OpenEye FRED) for pass/fail. Chain mixing via a random process. Prob(new is hit) / Prob(test is hit). (ed: missing details)
Works thru an example.
Start with parent cmpd. Sample an available attachment pt. Sample a random valid fragment for this attachment pt.
Start with parent cmpd. Sample an available attachment pt. Sample a random valid fragment for this attachment pt.
Molecule is treated as a graph. Then turned into MOL file, pH corrected, conformers generated, (RMSD filtering, and energy cutoff).
Describes docking details. Required to have metal chelate bond, and a particular H bond. Scored with chemgauss4.
Describes docking details. Required to have metal chelate bond, and a particular H bond. Scored with chemgauss4.
In less than 10 rounds, the chances of predicting a hit were much better than random. Overall results much better.
Comparison with previous work. Our predicted most active are substituted similarly to previous work. Also we find many of the same molecules.
Looking at improved procedure. Better docking - tried lots of different combinations of parameters. Problem we only have 30 known compounds as the benchmark. Reports r2 values (ed: not sure what this value represents?)
More thorough conformational sampling does not improve results, but is much slower. Maybe because exhaustive sampling just jams everything in something.
Mol Inform 2018, 37, 1
Rarey asks different meanings of active learning. Rather than optimising objective function, people optimise the information gain of the experiment.
