AI-Powered Virtual Screening of Large Compound Libraries Leads to the Discovery of Novel Inhibitors of Sirtuin-1

The Role of the Sirtuin Family Histone Deacetylases in Acute Myeloid Leukemia-A Promising Road Ahead

Acute myeloid leukemia (AML) corresponds to a heterogeneous group of clonal hematopoietic diseases, which are characterized by uncontrolled proliferation of malignant transformed myeloid precursors and their inability to differentiate into mature blood cells. The prognosis of AML depends on many variables, including the genetic features of the disease. Treatment outcomes, despite the introduction of new targeted therapies, are still unsatisfactory. Recently, there have been an increasing number of reports on enzymatic proteins of the sirtuin family and their potential importance in cancer in general. Sirtuins are a group of 7 (SIRT1-7) NAD+-dependent histone deacetylases with pleiotropic effects on metabolism, aging processes, and cell survival. They are not only responsible for post-translational modification of histones but also play various biochemical functions and interact with other proteins regulating cell survival, such as p53. Thus, their role in key mechanisms of tumorigenesis makes them a worthwhile topic in AML. Different sirtuins have been shown to act oppositely depending on the biological context, the mechanism of which requires further exploration. This review provides a comprehensive description of the significance and role of sirtuins in AML in light of the current state of knowledge. It focuses in particular on molecular mechanisms regulated by sirtuins and signaling pathways involved in leukemogenesis, as well as clinical aspects and potential therapeutic targets in AML.

The Need for Continuing Blinded Pose- and Activity Prediction Benchmarks

Computational tools for structure-based drug design (SBDD) are widely used in drug discovery and can provide valuable insights to advance projects in an efficient and cost-effective manner. However, despite the importance of SBDD to the field, the underlying methodologies and techniques have many limitations. In particular, binding pose and activity predictions (P-AP) are still not consistently reliable. We strongly believe that a limiting factor is the lack of a widely accepted and established community benchmarking process that independently assesses the performance and drives the development of methods, similar to the CASP benchmarking challenge for protein structure prediction. Here, we provide an overview of P-AP, unblinded benchmarking data sets, and blinded benchmarking initiatives (concluded and ongoing) and offer a perspective on learnings and the future of the field. To accelerate a breakthrough on the development of novel P-AP methods, it is necessary for the community to establish and support a long-term benchmark challenge that provides nonbiased training/test/validation sets, a systematic independent validation, and a forum for scientific discussions.

Activation and inhibition of sirtuins: From bench to bedside

The sirtuin family comprises seven NAD+-dependent enzymes which catalyze protein lysine deacylation and mono ADP-ribosylation. Sirtuins act as central regulators of genomic stability and gene expression and control key processes, including energetic metabolism, cell cycle, differentiation, apoptosis, and aging. As a result, all sirtuins play critical roles in cellular homeostasis and organism wellness, and their dysregulation has been linked to metabolic, cardiovascular, and neurological diseases. Furthermore, sirtuins have shown dichotomous roles in cancer, acting as context-dependent tumor suppressors or promoters. Given their central role in different cellular processes, sirtuins have attracted increasing research interest aimed at developing both activators and inhibitors. Indeed, sirtuin modulation may have therapeutic effects in many age-related diseases, including diabetes, cardiovascular and neurodegenerative disorders, and cancer. Moreover, isoform selective modulators may increase our knowledge of sirtuin biology and aid to develop better therapies. Through this review, we provide critical insights into sirtuin pharmacology and illustrate their enzymatic activities and biological functions. Furthermore, we outline the most relevant sirtuin modulators in terms of their modes of action, structure-activity relationships, pharmacological effects, and clinical applications.

EvaluationMaster: A GUI Tool for Structure-Based Virtual Screening Evaluation Analysis and Decision-Making Support

Structure-based virtual screening (SBVS) plays an indispensable role in the early phases of drug discovery, utilizing computational docking techniques to predict interactions between molecules and biological targets. During the SBVS process, selecting appropriate target structures and screening algorithms is crucial, as these choices significantly shape the outcomes. Typically, such selections require researchers to be proficient with multiple algorithms and familiar with evaluation and analysis processes, complicating their tasks. These algorithms' lack of graphical user interfaces (GUIs) further complicates it. To address these challenges, we introduced EvaluationMaster, the first GUI tool designed specifically to streamline and standardize the evaluation and decision-making processes in SBVS. It supports four docking algorithms' evaluation under multiple target structures and offers a comprehensive platform that manages the entire workflow─including the downloading of molecules, construction of decoy datasets, prediction of protein pockets, batch docking, and extensive data analysis. By automating complex evaluation tasks and providing clear visualizations of analysis results, EvaluationMaster significantly reduces the learning curve for researchers and boosts the efficiency of evaluations, potentially improving SBVS hit rates and accelerating the discovery and development of new therapeutic agents.

Exploring the landscape of post-translational modification in drug discovery

Post-translational modifications (PTMs) play a crucial role in regulating protein function, and their dysregulation is frequently associated with various diseases. The emergence of epigenetic drugs targeting factors such as histone deacetylases (HDACs) and histone methyltransferase enhancers of zeste homolog 2 (EZH2) has led to a significant shift towards precision medicine, offering new possibilities to overcome the limitations of traditional therapeutics. In this review, we aim to systematically explore how small molecules modulate PTMs. We discuss the direct targeting of enzymes involved in PTM pathways, the modulation of substrate proteins, and the disruption of protein-enzyme interactions that govern PTM processes. Additionally, we delve into the emerging strategy of employing multifunctional molecules to precisely regulate the modification levels of proteins of interest (POIs). Furthermore, we examine the specific characteristics of these molecules, evaluating their therapeutic benefits and potential drawbacks. The goal of this review is to provide a comprehensive understanding of PTM-targeting strategies and their potential for personalized medicine, offering a forward-looking perspective on the evolution of precision therapeutics.

GNN-DDAS: Drug discovery for identifying anti-schistosome small molecules based on graph neural network

Schistosomiasis is a tropical disease that poses a significant risk to hundreds of millions of people, yet often goes unnoticed. While praziquantel, a widely used anti-schistosome drug, has a low cost and a high cure rate, it has several drawbacks. These include ineffectiveness against schistosome larvae, reduced efficacy in young children, and emerging drug resistance. Discovering new and active anti-schistosome small molecules is therefore critical, but this process presents the challenge of low accuracy in computer-aided methods. To address this issue, we proposed GNN-DDAS, a novel deep learning framework based on graph neural networks (GNN), designed for drug discovery to identify active anti-schistosome (DDAS) small molecules. Initially, a multi-layer perceptron was used to derive sequence features from various representations of small molecule SMILES. Next, GNN was employed to extract structural features from molecular graphs. Finally, the extracted sequence and structural features were then concatenated and fed into a fully connected network to predict active anti-schistosome small molecules. Experimental results showed that GNN-DDAS exhibited superior performance compared to the benchmark methods on both benchmark and real-world application datasets. Additionally, the use of GNNExplainer model allowed us to analyze the key substructure features of small molecules, providing insight into the effectiveness of GNN-DDAS. Overall, GNN-DDAS provided a promising solution for discovering new and active anti-schistosome small molecules.

Recent advances from computer-aided drug design to artificial intelligence drug design

Computer-aided drug design (CADD), a cornerstone of modern drug discovery, can predict how a molecular structure relates to its activity and interacts with its target using structure-based and ligand-based methods. Fueled by ever-increasing data availability and continuous model optimization, artificial intelligence drug design (AIDD), as an enhanced iteration of CADD, has thrived in the past decade. AIDD demonstrates unprecedented opportunities in protein folding, property prediction, and molecular generation. It can also facilitate target identification, high-throughput screening (HTS), and synthetic route prediction. With AIDD involved, the process of drug discovery is greatly accelerated. Notably, AIDD offers the potential to explore uncharted territories of chemical space beyond current knowledge. In this perspective, we began by briefly outlining the main workflows and components of CADD. Then through showcasing exemplary cases driven by AIDD in recent years, we describe the evolving role of artificial intelligence (AI) in drug discovery from three distinct stages, that is, chemical library screening, linker generation, and de novo molecular generation. In this process, we attempted to draw comparisons between the features of CADD and AIDD.

Perspectives on current approaches to virtual screening in drug discovery

INTRODUCTION

For the past two decades, virtual screening (VS) has been an efficient hit finding approach for drug discovery. Today, billions of commercially accessible compounds are routinely screened, and many successful examples of VS have been reported. VS methods continue to evolve, including machine learning and physics-based methods.

AREAS COVERED

The authors examine recent examples of VS in drug discovery and discuss prospective hit finding results from the critical assessment of computational hit-finding experiments (CACHE) challenge. The authors also highlight the cost considerations and open-source options for conducting VS and examine chemical space coverage and library selections for VS.

EXPERT OPINION

The advancement of sophisticated VS approaches, including the use of machine learning techniques and increased computer resources as well as the ease of access to synthetically available chemical spaces, and commercial and open-source VS platforms allow for interrogating ultra-large libraries (ULL) of billions of molecules. An impressive number of prospective ULL VS campaigns have generated potent and structurally novel hits across many target classes. Nonetheless, many successful contemporary VS approaches still use considerably smaller focused libraries. This apparent dichotomy illustrates that VS is best conducted in a fit-for-purpose way choosing an appropriate chemical space. Better methods need to be developed to tackle more challenging targets.

Ligand-Based Virtual Screening as a Path to New Chemotypes for Candidate PET Radioligands for Imaging Tauopathies

Ligand-based virtual screening (LBVS) has rarely been tested as a method for discovering new structural scaffolds for PET radioligand development. This study used LBVS to discover potential chemotype leads for developing radioligands for PET imaging of tauopathies. ZINC12, a free database of over 12 million commercially available compounds, was searched to discover novel scaffolds based on similarities to four query compounds. Thirteen high-ranking hits were purchased and assayed for their ability to compete against three tritiated radioligands at their distinct binding sites in Alzheimer's disease brain tissue. Three hits were 2-substituted 6-methoxy naphthalenes. Synthetic elaboration of this new chemotype yielded three new ligands (25, 26, and 28) with high affinity for the [3H]6 (flortaucipur) neurofibrillary tangle binding site. Compound 28 showed remarkably high affinity (Ki, 7 nM) and other desirable properties for a candidate PET radioligand, including low topological polar surface area, moderate computed log D, and amenability for labeling with carbon-11. LBVS appears to be uniquely valuable for discovering new chemotypes for candidate PET radioligands.

The diagnostic, therapeutic, and ethical impact of artificial intelligence in modern medicine

In the evolution of modern medicine, artificial intelligence (AI) has been proven to provide an integral aspect of revolutionizing clinical diagnosis, drug discovery, and patient care. With the potential to scrutinize colossal amounts of medical data, radiological and histological images, and genomic data in healthcare institutions, AI-powered systems can recognize, determine, and associate patterns and provide impactful insights that would be strenuous and challenging for clinicians to detect during their daily clinical practice. The outcome of AI-mediated search offers more accurate, personalized patient diagnoses, guides in research for new drug therapies, and provides a more effective multidisciplinary treatment plan that can be implemented for patients with chronic diseases. Among the many promising applications of AI in modern medicine, medical imaging stands out distinctly as an area with tremendous potential. AI-powered algorithms can now accurately and sensitively identify cancer cells and other lesions in medical images with greater accuracy and sensitivity. This allows for earlier diagnosis and treatment, which can significantly impact patient outcomes. This review provides a comprehensive insight into diagnostic, therapeutic, and ethical issues with the advent of AI in modern medicine.

Targeting Sirtuin 1 for therapeutic potential: Drug repurposing approach integrating docking and molecular dynamics simulations

Identifying novel therapeutic agents is a fundamental challenge in contemporary drug development, especially in the context of complex diseases like cancer, neurodegenerative disorders, and metabolic syndromes. Here, we present a comprehensive computational study to identify potential inhibitors of SIRT1 (Sirtuin 1), a critical protein involved in various cellular processes and disease pathways. Leveraging the concept of drug repurposing, we employed a multifaceted approach that integrates molecular docking and molecular dynamics (MD) simulations to predict the binding affinities and dynamic behavior of a diverse set of FDA-approved drugs from DrugBank against the SIRT1. Initially, compounds were shortlisted based on their binding affinities and interaction analyses to identify safe and promising binding partners for SIRT1. Among these candidates, Doxercalciferol and Timiperone emerged as potential candidates, displaying notable affinity, efficiency, and specificity towards the binding pocket of SIRT1. Extensive evaluation revealed that these identified compounds boast a range of favorable biological properties and prefer binding to the active site of SIRT1. To delve deeper into the interactions, all-atom MD simulations were conducted for 500 nanoseconds (ns). These simulations assessed the conformational dynamics, stability, and interaction mechanism of the SIRT1-Doxercalciferol and SIRT1-Timiperone complexes. The MD simulations illustrated that the SIRT1-Doxercalciferol and SIRT1-Timiperone complexes maintain stability over a 500 ns trajectory. These insightful outcomes propose that Doxercalciferol and Timiperone hold promise as viable scaffolds for developing potential SIRT1 inhibitors, with implications for tackling complex diseases such as cancer, neurodegenerative disorders, and metabolic syndromes.