High-throughput ligand screening via preclustering and evolved neural networks.
The pathway for novel lead drug discovery has many major deficiencies, the most significant of which is the immense size of small molecule diversity space. Methods that increase the search efficiency and/or reduce the size of the search space, increase the rate at which useful lead compounds are identified. Artificial neural networks optimized via evolutionary computation provide a cost and time-effective solution to this problem. Here, we present results that suggest preclustering of small molecules prior to neural network optimization is useful for generating models of quantitative structure-activity relationships for a set of HIV inhibitors. Using these methods, it is possible to prescreen compounds to separate active from inactive compounds or even actives and mildly active compounds from inactive compounds with high predictive accuracy while simultaneously reducing the feature space. It is also possible to identify "human interpretable" features from the best models that can be used for proposal and synthesis of new compounds in order to optimize potency and specificity.
David
D
Hecht
Gary
G
Fogel
D019380:Anti-HIV Agents / Q000737:chemistry* / Q000145:classification; D001185:Artificial Intelligence; D016000:Cluster Analysis; D020650:Combinatorial Chemistry Techniques / Q000379:methods*; D003198:Computer Simulation; D015195:Drug Design; D008024:Ligands; D008956:Models, Chemical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D021281:Quantitative Structure-Activity Relationship
D019380:Anti-HIV Agents; D008024:Ligands