Theoretical approaches to identify the potent scaffold for human sirtuin1 activator: Bayesian modeling and density functional theory

Density functional theory (DFT) studies in HDAC-based chemotherapeutics: Current findings, case studies and future perspectives

Density Functional Theory (DFT) is a quantum chemical computational method used to predict and analyze the electronic properties of atoms, molecules, and solids based on the density of electrons rather than wavefunctions. It provides insights into the structure, bonding, and behavior of different molecules, including those involved in the development of chemotherapeutic agents, such as histone deacetylase inhibitors (HDACis). HDACs are a wide group of metalloenzymes that facilitate the removal of acetyl groups from acetyl-lysine residues situated in the N-terminal tail of histones. Abnormal HDAC recruitment has been linked to several human diseases, especially cancer. Therefore, it has been recognized as a prospective target for accelerating the development of anticancer therapies. Researchers have studied HDACs and its inhibitors extensively using a combination of experimental methods and diverse in-silico approaches such as machine learning and quantitative structure-activity relationship (QSAR) methods, molecular docking, molecular dynamics, pharmacophore mapping, and more. In this context, DFT studies can make significant contribution by shedding light on the molecular properties, interactions, reaction pathways, transition states, reactivity and mechanisms involved in the development of HDACis. This review attempted to elucidate the scope in which DFT methodologies may be used to enhance our comprehension of the molecular aspects of HDAC inhibitors, aiding in the rational design and optimization of these compounds for therapeutic applications in cancer and other ailments. The insights gained can guide experimental efforts toward developing more potent and selective HDAC inhibitors.

Virtual Screening in the Identification of Sirtuins’ Activity Modulators

Sirtuins are NAD⁺-dependent deac(et)ylases with different subcellular localization. The sirtuins’ family is composed of seven members, named SIRT-1 to SIRT-7. Their substrates include histones and also an increasing number of different proteins. Sirtuins regulate a wide range of different processes, ranging from transcription to metabolism to genome stability. Thus, their dysregulation has been related to the pathogenesis of different diseases. In this review, we discussed the pharmacological approaches based on sirtuins’ modulators (both inhibitors and activators) that have been attempted in in vitro and/or in in vivo experimental settings, to highlight the therapeutic potential of targeting one/more specific sirtuin isoform(s) in cancer, neurodegenerative disorders and type 2 diabetes. Extensive research has already been performed to identify SIRT-1 and -2 modulators, while compounds targeting the other sirtuins have been less studied so far. Beside sections dedicated to each sirtuin, in the present review we also included sections dedicated to pan-sirtuins’ and to parasitic sirtuins’ modulators. A special focus is dedicated to the sirtuins’ modulators identified by the use of virtual screening.

Improved Deep Learning Based Method for Molecular Similarity Searching Using Stack of Deep Belief Networks

Virtual screening (VS) is a computational practice applied in drug discovery research. VS is popularly applied in a computer-based search for new lead molecules based on molecular similarity searching. In chemical databases similarity searching is used to identify molecules that have similarities to a user-defined reference structure and is evaluated by quantitative measures of intermolecular structural similarity. Among existing approaches, 2D fingerprints are widely used. The similarity of a reference structure and a database structure is measured by the computation of association coefficients. In most classical similarity approaches, it is assumed that the molecular features in both biological and non-biologically-related activity carry the same weight. However, based on the chemical structure, it has been found that some distinguishable features are more important than others. Hence, this difference should be taken consideration by placing more weight on each important fragment. The main aim of this research is to enhance the performance of similarity searching by using multiple descriptors. In this paper, a deep learning method known as deep belief networks (DBN) has been used to reweight the molecule features. Several descriptors have been used for the MDL Drug Data Report (MDDR) dataset each of which represents different important features. The proposed method has been implemented with each descriptor individually to select the important features based on a new weight, with a lower error rate, and merging together all new features from all descriptors to produce a new descriptor for similarity searching. Based on the extensive experiments conducted, the results show that the proposed method outperformed several existing benchmark similarity methods, including Bayesian inference networks (BIN), the Tanimoto similarity method (TAN), adapted similarity measure of text processing (ASMTP) and the quantum-based similarity method (SQB). The results of this proposed multi-descriptor-based on Stack of deep belief networks method (SDBN) demonstrated a higher accuracy compared to existing methods on structurally heterogeneous datasets.

Ligand and structure-based approaches for the identification of SIRT1 activators

SIRT1 is a NAD(+)-dependent deacetylase that involved in various important metabolic pathways. Combined ligand and structure-based approach was utilized for identification of SIRT1 activators. Pharmacophore models were developed using DISCOtech and refined with GASP module of Sybyl X software. Pharmacophore models were composed of two hydrogen bond acceptor (HBA) atoms, two hydrogen bond donor (HBD) sites and one hydrophobic (HY) feature. The pharmacophore models were validated through receiver operating characteristic (ROC) and Güner-Henry (GH) scoring methods. Model-2 was selected as best model among the model 1-3, based on ROC and GH score value, and found reliable in identification of SIRT1 activators. Model-2 (3D search query) was searched against Zinc database. Several compounds with different chemical scaffold were retrieved as hits. Currently, there is no experimental SIRT1 3D structure available, therefore, we modeled SIRT1 protein structure using homology modeling. Compounds with Qfit value of more than 86 were selected for docking study into the SIRT1 homology model to explore the binding mode of retrieved hits in the active allosteric site. Finally, in silico ADMET prediction study was performed with two best docked compounds. Combination of ligand and structure-based modeling methods identified active hits, which may be good lead compounds to develop novel SIRT1 activators.