Machine Learning for Sequence and Structure-Based Protein–Ligand Interaction Prediction

Abstract

Developing new drugs is too expensive and time -consuming. Accurately predicting the interaction between drugs and targets will likely change how the drug is discovered. Machine learning-based protein−ligand interaction prediction has demonstrated significant potential. In this paper, computational methods, focusing on sequence and structure to study protein−ligand interactions, are examined. Therefore, this paper starts by presenting an overview of the data sets applied in this area, as well as the various approaches applied for representing proteins and ligands. Then, sequence-based and structure-based classification criteria are subsequently utilized to categorize and summarize both the classical machine learning models and deep learning models employed in protein−ligand interaction studies. Moreover, the evaluation methods and interpretability of these models are proposed. Furthermore, delving into the diverse applications of protein−ligand interaction models in drug research is presented. Lastly, the current challenges and future directions in this field are addressed.

A Reversible Spherical Geometric Conversion of Protein Backbone Structure Coordinate Matrix to Three Independent Vectors of ρ, θ and φ

“…Overall, AlphaFold combines advanced machine learning algorithms with biological insights to accurately predict protein structures, significantly advancing our understanding of biology and opening new avenues for drug discovery and biotechnology. While current algorithms in protein structure prediction have made significant strides, particularly with the advent of machine learning techniques, enhanced sampling methods, and improved force fields [61][62][63][64][65][66][67][68], further improvement in protein structure prediction algorithms is necessary for several reasons: 1. Accuracy for large proteins and complexes [69-71]: while current algorithms perform reasonably well for small to medium-sized proteins, accurately predicting the structures of large proteins or protein complexes remains challenging.…”

Predicting binding events in very flexible, allosteric, multi-domain proteins

“…[4][5][6] The rapid increase in the number of experimentally resolved protein structures has fueled in the last decades the development of a large variety of computational tools to mine their conformational space, including machine/deeplearning approaches integrating experimental data and simulations. [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] Methods to mimic protein-ligand association in silico, such as molecular docking and virtual screening, have become routinary ingredients in any modern drug design lab. 2,27,28 Among possible applications a key one concerns the prediction of the interactions between proteins and small molecules (ligands), which underlies chemotherapy and drug design.…”