Cluster: Computational Chemistry

Computational Chemistry is a branch of chemistry using software to simulate and attempt to solve issues faced by the chemistry industry. It uses a combination of theoretical chemistry and simulation to help determine the structures and properties of molecules. Computational chemists integrate mathematical algorithms, statistics, databases, and experimental observations to develop chemical simulations and modelling computations. Examples of the applications of computational chemistry include: identifying the drug binding sites of drugs, creation of how kinetics and thermodynamics effects chemical synthesis reactions, and the scientific exploration of physical processes (superconductivity, energy storage, corrosion, or phase changes) of molecules during chemical reactions. Computation chemistry approaches are used to develop catalysts for sustainable fuel and chemical production.

Different areas of computational chemistry are concerned with the exploration of chemical space which is defined as the set of all possible organic compounds. The virtual chemical space has 10⁶³ possible organic compounds of 30 atoms in size. Mapping and visualization of chemical space are areas of research that aim to provide meaningful representations of chemical space.

• Quantitative structure activity relationship (QSAR) where the output to be predicted is usually the biological activity of the compound
• Deep neural network (DNN)-based QSAR models

Protein contact prediction is the prediction of spatial proximity of any two residues of a protein sequence when it is folded as its 3D structure.

• Long timescale molecular dynamics (physics-based simulations)
• Knowledge-based physical approaches
• Machine learning (ANNs, SVMs and hidden Markov model)

Quantum chemistry is the application of quantum mechanics to the theoretical study of chemical systems. Machine learning is applied to quantum chemistry to supplement or replace traditional quantum mechanical calculations.

• Amazon Braket is an Amazon Web Services (subsidiary of Amazon) service for developer and researchers. Customers can explore and design, test and troubleshoot quantum algorithms on simulated quantum computers. Then customers are able to use Amazon Bracket to run their quantum algorithms on quantum processors including D-Wave, IonQ and Rigetti.

Potential electrocatalysts for sustainable fuel and chemical production can be tested for sustainability and efficiency using computational quantum chemistry. Quantum chemistry computing simulations were used to categorize hypothetical electrocatalysts that are too slow or too expensive.

• BindingDB is a public, web-accessible database of measured binding affinities, focused on interactions between potential protein targets and small molecules
• ChEMBL is maintained by the European Bioinformatics Institute, of the European Molecular Biology Laboratory, based at the Wellcome Trust Genome Campus, Hinxton, UK
• Generated database GDB17, the chemical universe database, enumerates 166.4 billion possible molecules up to 17 atoms of C, N,O, S and halogens following rules for chemical stability, synthetic feasibility, and medicinal chemistry.
• GDBMedChem is a collection of 10 million small molecules from GDB17 inspired by medicinal chemistry and excludes problematic functional groups and complex molecules
• PubChem is a database of chemical molecules and their activities against biological assays. The system is maintained by the National Center for Biotechnology Information, a component of the National Library of Medicine, which is part of the United States National Institutes of Health

• Gabedit is a freeware graphical user interface with tools for editing, displaying, analyzing, converting, and animating molecular systems
• ioChem-BD is a tool for managing large volumes of quantum chemistry results from a diverse group of simulation packages
• MOLCAS is a program system for calculations of electronic and structural properties of molecular systems in gas, liquid or solid phase developed by scientists at the Lund University, Sweden

• Density functional theory (DFT)
• Random-phase approximation (RPA)
• QM/MM and QM/MM-MD molecular techniques enable precise descriptions of biological phenomena and reactions

Competitions make use of gamification and crowd sourcing for analysis of data.

• CASP (Critical Assessment of Structure Prediction) aim to advance the state of the art in modeling protein structure from amino acid sequence. Participants are invited to submit models for a set of proteins for which the experimental structures are not yet public. Assessments and results are published in a special issue of the journal PROTEINS.
• The Merck Molecular Activity Challenge (2012), with a list of compounds of known activity in a given assay the challenge was to recapitulate the data through simulation. The competition was won by a team of academics in the Kaggle community and located at University of Toronto and University of Washington. The team used deep learning.
• Predicting a Biological Response competition (2012) by Boehringer working through Kaggle provided training data comprised of molecular characteristics and experimental data with the challenge to create the best algorithm to build predictive models. The winning team included two research directors at an insurance firm and a neurobiologist from Harvard University.
• Tox21 Data Challenge (2014) was a challenge to build predictive models of nuclear receptor and stress response pathways as mediated by exposure to environmental toxicants and drugs. The Tox21 Program (Toxicology in the 21st Century) is a collaboration between U.S. federal agencies including the NIH to characterize potential toxicity of chemicals.