Synergy between machine learning and natural products cheminformatics: Application to the lead discovery of anthraquinone derivatives

Abstract

Cheminformatics utilizing machine learning (ML) techniques have opened up a new horizon in drug discovery. This is owing to vast chemical space expansion with rocketing numbers of expected hits and lead compounds that match druggable macromolecular targets, in particular from natural compounds. Due to the natural products' (NP) structural complexity, uniqueness, and diversity, they could occupy a bigger space in pharmaceuticals, allowing the industry to pursue more selective leads in the nanomolar range of binding affinity. ML is an essential part of each step of the drug design pipeline, such as target prediction, compound library preparation, and lead optimization. Notably, molecular mechanic and dynamic simulations, induced docking, and free energy perturbations are essential in predicting best binding poses, binding free energy values, and molecular mechanics force fields. Those applications have leveraged from artificial intelligence (AI), which decreases the computational costs required for such costly simulations. This review aimed to describe chemical space and compound libraries related to NPs. High-throughput screening utilized for fractionating NPs and high-throughput virtual screening and their strategies, and significance, are reviewed. Particular emphasis was given to AI approaches, ML tools, algorithms, and techniques, especially in drug discovery of macrocyclic compounds and approaches in computer-aided and ML-based drug discovery. Anthraquinone derivatives were discussed as a source of new lead compounds that can be developed using ML tools for diverse medicinal uses such as cancer, infectious diseases, and metabolic disorders. Furthermore, the power of principal component analysis in understanding relevant protein conformations, and molecular modeling of protein-ligand interaction were also presented. Apart from being a concise reference for cheminformatics, this review is a useful text to understand the application of ML-based algorithms to molecular dynamics simulation and in silico absorption, distribution, metabolism, excretion, and toxicity prediction.

Escin enhanced the efficacy of sorafenib by autophagy‐mediated apoptosis in lung cancer cells

“…When the combined effect is significantly higher than that predicted by alone potencies, the combination is said to be synergistic. A synergistic interaction allows the usage of lower doses of the combination constituents, which may reduce adverse reactions (Moshawih et al, 2022; Torkamannia et al, 2022). Here first, Chou–Talaly's algorithm‐based software Campusyn was used to predict and analyse the type of combinational effect.…”

Applications of Machine Learning From Constructing the Database to the Lead Discovery: Practical Approaches on Cancer-Related Proteins

“…Said MOSHAWIH 1* , Long Chaiu MING 1,2 , Nurolaini KIFLI 1 , Hui Poh GOH 1 1 PAP Rashidah Sa'adatul Bolkiah Institute of Health Sciences, Universiti Brunei Darussalam, Gadong, Brunei Darussalam 2 School of Medical and Life Sciences, Sunway University, Sunway City 47500, Malaysia saeedmomo@hotmail.com *Presenting and corresponding author Drug discovery using advanced computational tools such as machine learning has succeeded in reducing about 40% and 60% of the time and costs required by conventional drug discovery pipelines respectively. In this study we aim at building a combinatorial library of anthraquinone and chalcone derivative and producing workflow of different screening and scoring methodologies to find hits against cancerrelated proteins, and examine them using molecular dynamic and mechanics simulations.…”

Insights into the computer-aided drug design and discovery based on anthraquinone scaffold for cancer treatment: A systematic review

“…With today’s advancement in computational power, only a few seconds are needed to construct a virtual combinatorial library with millions of compounds [ 22 ]. Combinatorial library has been used in natural product research to create databases of natural product analogues with drug-like properties [ 23 ]. This strategy can uncover the potential of natural products with privileged scaffold for new drug design and discovery, for instance, the anthraquinone- and chalcone- derivatives that showed a wide spectrum of biological effects on many different macromolecular targets responsible for human diseases including cancer were used to construct virtual library as the starting point of new drug research [ 24 ].…”