Faculty Researchers
Computational chemistry is a branch of chemistry that uses computer simulations or descriptor-based machine learning/artificial intelligence techniques to assist in solving chemical problems. Scientists incorporate methods of theoretical chemistry into efficient computer programs to calculate the structures and properties of molecules and solids. While computational results normally complement the information obtained by chemical experiments, it can also be used to predict previously unobserved chemical phenomena. Furthermore, computational methods are extremely useful in gaining insights on a molecular level on structures formed and potential mechanisms involved. Also, information from computational methods are used to guide experimentalists in what experimental variables to change in planning future experiments to, for example, design new drugs and materials with improved performance. Major research areas in which computational chemistry is used include: i) prediction of molecular structure of molecules by quantum chemical methods; ii) to store and search for data on chemical entities; iii) to reveal chemical structure-property correlations (QSPR) as well as quantitative structure–activity relationship (QSAR) and iv) computational approaches to design molecules that interact in specific ways with other molecules (e.g. drug and catalyst). Cheminformatics focuses on extracting, processing and extrapolating meaningful data from chemical structures. With the rapid explosion of chemical ‘big’ data from high-throughput screening and combinatorial synthesis, machine learning is critically important for use by, for example, drug designers to mine chemical information from large compound databases to design drugs with important biological properties. Researchers in the Department of Chemistry and Chemical Biology are using Cheminformatics and Computational Chemistry to understand mechanisms of protein-structure interactions, develop new molecular property descriptors and machine learning methods, create fully validated predictive property models. Current application areas for this work include pharmaceutical absorption, distribution, metabolism, elimination (ADME) predictions, virtual high-throughput screening of drug candidates, protein chromatography modeling and polymer property predictions.
