Identification of novel potential scaffold for class I HDACs inhibition: An in-silico protocol based on virtual screening, molecular dynamics, mathematical analysis and machine learning

Artificial intelligence for small molecule anticancer drug discovery

INTRODUCTION

The transition from conventional cytotoxic chemotherapy to targeted cancer therapy with small-molecule anticancer drugs has enhanced treatment outcomes. This approach, which now dominates cancer treatment, has its advantages. Despite the regulatory approval of several targeted molecules for clinical use, challenges such as low response rates and drug resistance still persist. Conventional drug discovery methods are costly and time-consuming, necessitating more efficient approaches. The rise of artificial intelligence (AI) and access to large-scale datasets have revolutionized the field of small-molecule cancer drug discovery. Machine learning (ML), particularly deep learning (DL) techniques, enables the rapid identification and development of novel anticancer agents by analyzing vast amounts of genomic, proteomic, and imaging data to uncover hidden patterns and relationships.

AREA COVERED

In this review, the authors explore the important landmarks in the history of AI-driven drug discovery. They also highlight various applications in small-molecule cancer drug discovery, outline the challenges faced, and provide insights for future research.

EXPERT OPINION

The advent of big data has allowed AI to penetrate and enable innovations in almost every stage of medicine discovery, transforming the landscape of oncology research through the development of state-of-the-art algorithms and models. Despite challenges in data quality, model interpretability, and technical limitations, advancements promise breakthroughs in personalized and precision oncology, revolutionizing future cancer management.

3D QSAR pharmacophore based lead identification of G9a lysine methyltransferase towards epigenetic therapeutics

The G9a, Lysine Methyltransferase that methylates the histone 3 lysine 9 (H3K9) of the nucleosome, is an excellent epigenetic target having no clinically passed inhibitor currently owing to adverse in vivo ADMET toxicities. In this work, we have carried out detailed computational investigations to find novel and safer lead against the target using advanced 3 D QSAR pharmacophore screening of databases containing more than 400000 entrees of natural compounds. The screening was conducted at different levels at increasing stringencies by employing pharmacophore mapping, druglikenesses and interaction profiles of the selected to identify potential hit compounds. The potential hits were further screened by advanced flexible docking, ADME and toxicity analysis to eight hit compounds. Based on the comparative analysis of the hits with the reference inhibitor, we identified one lead inhibitor against the G9a, having better binding efficacy and a safer ADMET profile than the reference inhibitor. Finally, the results were further verified using robust molecular dynamics simulation and MM-GBSA binding energy calculation. The natural compounds are generally considered benign due to their long human uses and this is the first attempt of in silico screening of a large natural compound library against G9a to our best knowledge. Therefore, the finding of this study may add value towards the development of epigenetic therapeutics against the G9a.Communicated by Ramaswamy H. Sarma.

Artificial intelligence in virtual screening: models versus experiments

A typical drug discovery project involves identifying active compounds with significant binding potential for selected disease-specific targets. Experimental high-throughput screening (HTS) is a traditional approach to drug discovery, but is expensive and time-consuming when dealing with huge chemical libraries with billions of compounds. The search space can be narrowed down with the use of reliable computational screening approaches. In this review, we focus on various machine-learning (ML) and deep-learning (DL)-based scoring functions developed for solving classification and ranking problems in drug discovery. We highlight studies in which ML and DL models were successfully deployed to identify lead compounds for which the experimental validations are available from bioassay studies.

A review on machine learning approaches and trends in drug discovery

Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.

Application of Machine Learning Techniques to Predict Binding Affinity for Drug Targets: A Study of Cyclin-Dependent Kinase 2

BACKGROUND

The elucidation of the structure of cyclin-dependent kinase 2 (CDK2) made it possible to develop targeted scoring functions for virtual screening aimed to identify new inhibitors for this enzyme. CDK2 is a protein target for the development of drugs intended to modulate cellcycle progression and control. Such drugs have potential anticancer activities.

OBJECTIVE

Our goal here is to review recent applications of machine learning methods to predict ligand- binding affinity for protein targets. To assess the predictive performance of classical scoring functions and targeted scoring functions, we focused our analysis on CDK2 structures.

METHODS

We have experimental structural data for hundreds of binary complexes of CDK2 with different ligands, many of them with inhibition constant information. We investigate here computational methods to calculate the binding affinity of CDK2 through classical scoring functions and machine- learning models.

RESULTS

Analysis of the predictive performance of classical scoring functions available in docking programs such as Molegro Virtual Docker, AutoDock4, and Autodock Vina indicated that these methods failed to predict binding affinity with significant correlation with experimental data. Targeted scoring functions developed through supervised machine learning techniques showed a significant correlation with experimental data.

CONCLUSION

Here, we described the application of supervised machine learning techniques to generate a scoring function to predict binding affinity. Machine learning models showed superior predictive performance when compared with classical scoring functions. Analysis of the computational models obtained through machine learning could capture essential structural features responsible for binding affinity against CDK2.

Integrating Machine Learning-Based Virtual Screening With Multiple Protein Structures and Bio-Assay Evaluation for Discovery of Novel GSK3β Inhibitors

Glycogen synthase kinase-3β (GSK3β) is associated with various key biological processes, and it has been considered as a critical therapeutic target for the treatment of many diseases. However, it is a big challenge to develop ATP-competition GSK3β inhibitors because of the high sequence homology with other kinases. In this work, a novel parallel virtual screening strategy based on multiple GSK3β protein structures, integrating molecular docking, complex-based pharmacophore, and naive Bayesian classification, was developed to screen a large chemical database, the 50 compounds with top-scores then underwent a luminescent kinase assay, which led to the discovery of two GSK3β inhibitor hits. The high screening enrichment rate indicates the reliability and practicability of the integrated protocol. Finally, molecular docking and molecular dynamics simulation were employed to investigate the binding modes of the GSK3β inhibitors, and some “hot residues” critical to GSK3β affinity were highlighted. The present study may provide some valuable guidance for the development of novel GSK3β inhibitors.

Prognosis Analysis of Histone Deacetylases mRNA Expression in Ovarian Cancer Patients

Histone deacetylases modulate the dynamic balance of histone acetylation and deacetylation in cells, which participate in epigenetic regulations. Accumulated evidence has demonstrated that histone deacetylases are associated with angiogenesis, cell proliferation and survival in a variety of human cancers. However, the expression and distinct prognostic value of histone deacetylases in ovarian cancer have not been well elucidated. In the present study, we collected the overall survival (OS), progress free survival (PFS), and histone deacetylases (HDAC1-11) mRNA expression in ovarian cancer from the Kaplan-Meier plotter online database. We investigated the relationship between histone deacetylases mRNA level and the clinicopathological parameters of the ovarian cancer patients, such as histology subtypes, clinical stages, grades and TP53 mutation. Our analysis data showed that over-expression of HDAC1, HDAC2, HDAC4, HDAC5 and HDAC11 were correlated to poor overall survival and unfavorable progress free survival in all ovarian cancer patients. Notably, the higher level of HDAC11 was associated with the worse OS and PFS for serous/ stage III+IV/ grade III/ TP53 mutation ovarian cancer patients. In conclusion, HDACs may play a crucial role in the prognosis of ovarian cancer, but it is worth noting that HDAC11 may be a biomarker for poor prognosis in ovarian cancer patients.