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Issue 8, 2020
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A review of mathematical representations of biomolecular data

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Recently, machine learning (ML) has established itself in various worldwide benchmarking competitions in computational biology, including Critical Assessment of Structure Prediction (CASP) and Drug Design Data Resource (D3R) Grand Challenges. However, the intricate structural complexity and high ML dimensionality of biomolecular datasets obstruct the efficient application of ML algorithms in the field. In addition to data and algorithm, an efficient ML machinery for biomolecular predictions must include structural representation as an indispensable component. Mathematical representations that simplify the biomolecular structural complexity and reduce ML dimensionality have emerged as a prime winner in D3R Grand Challenges. This review is devoted to the recent advances in developing low-dimensional and scalable mathematical representations of biomolecules in our laboratory. We discuss three classes of mathematical approaches, including algebraic topology, differential geometry, and graph theory. We elucidate how the physical and biological challenges have guided the evolution and development of these mathematical apparatuses for massive and diverse biomolecular data. We focus the performance analysis on protein–ligand binding predictions in this review although these methods have had tremendous success in many other applications, such as protein classification, virtual screening, and the predictions of solubility, solvation free energies, toxicity, partition coefficients, protein folding stability changes upon mutation, etc.

Graphical abstract: A review of mathematical representations of biomolecular data

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Article information

03 Dec 2019
17 Jan 2020
First published
22 Jan 2020

Phys. Chem. Chem. Phys., 2020,22, 4343-4367
Article type

A review of mathematical representations of biomolecular data

D. D. Nguyen, Z. Cang and G. Wei, Phys. Chem. Chem. Phys., 2020, 22, 4343
DOI: 10.1039/C9CP06554G

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