A prospective compound screening contest identified broader inhibitors for Sirtuin 1

Mothra: Multiobjective de novo Molecular Generation Using Monte Carlo Tree Search

In the field of drug discovery, identifying compounds that satisfy multiple criteria, such as target protein affinity, pharmacokinetics, and membrane permeability, is challenging because of the vast chemical space. Until now, multiobjective optimization via generative models has often involved linear combinations of different reward functions. Linear combinations solve multiobjective optimization problems by turning multiobjective optimization into a single-objective task and causing problems with weighting for each objective. Herein, we propose a scalable multiobjective molecular generative model developed using deep learning techniques. This model integrates the capabilities of recurrent neural networks for molecular generation and Pareto multiobjective Monte Carlo tree search to determine the optimal search direction. Through this integration, our model can generate compounds using enhanced evaluation functions that include important aspects like target protein affinity, drug similarity, and toxicity. The proposed model addresses the limitations of previous linear combination methods, and its effectiveness is demonstrated via extensive experimentation. The improvements achieved in the evaluation metrics underscore the potential utility of our approach toward drug discovery applications. In addition, we provide the source code for our model such that researchers can easily access and use our framework in their own investigations. The source code and pretrained model for Mothra, developed in this study, along with the Docker image for the Pareto front explorer and compound picker, designed to streamline the selection and visualization of optimal chemical compounds, are released under the GNU General Public License v3.0 and available at https://github.com/sekijima-lab/Mothra.

Recent Advances in the Discovery of SIRT1/2 Inhibitors via Computational Methods: A Perspective

Sirtuins (SIRTs) are classified as class III histone deacetylases (HDACs), a family of enzymes that catalyze the removal of acetyl groups from the ε-N-acetyl lysine residues of histone proteins, thus counteracting the activity performed by histone acetyltransferares (HATs). Based on their involvement in different biological pathways, ranging from transcription to metabolism and genome stability, SIRT dysregulation was investigated in many diseases, such as cancer, neurodegenerative disorders, diabetes, and cardiovascular and autoimmune diseases. The elucidation of a consistent number of SIRT-ligand complexes helped to steer the identification of novel and more selective modulators. Due to the high diversity and quantity of the structural data thus far available, we reviewed some of the different ligands and structure-based methods that have recently been used to identify new promising SIRT1/2 modulators. The present review is structured into two sections: the first includes a comprehensive perspective of the successful computational approaches related to the discovery of SIRT1/2 inhibitors (SIRTIs); the second section deals with the most interesting SIRTIs that have recently appeared in the literature (from 2017). The data reported here are collected from different databases (SciFinder, Web of Science, Scopus, Google Scholar, and PubMed) using "SIRT", "sirtuin", and "sirtuin inhibitors" as keywords.

Gargoyles: An Open Source Graph-Based Molecular Optimization Method Based on Deep Reinforcement Learning

Automatic optimization methods for compounds in the vast compound space are important for drug discovery and material design. Several machine learning-based molecular generative models for drug discovery have been proposed, but most of these methods generate compounds from scratch and are not suitable for exploring and optimizing user-defined compounds. In this study, we developed a compound optimization method based on molecular graphs using deep reinforcement learning. This method searches for compounds on a fragment-by-fragment basis and at high density by generating fragments to be added atom by atom. Experimental results confirmed that the quantum electrodynamics (QED), the optimization target set in this study, was enhanced by searching around the starting compound. As a use case, we successfully enhanced the activity of a compound by targeting dopamine receptor D2 (DRD2). This means that the generated compounds are not structurally dissimilar from the starting compounds, as well as increasing their activity, indicating that this method is suitable for optimizing molecules from a given compound. The source code is available at https://github.com/sekijima-lab/GARGOYLES.

Sirtuin 1 inhibition: a promising avenue to suppress cancer progression through small inhibitors design

SIRT1 is a protein associated with vital cell functions such as gene regulation, metabolism, ageing, and cellular energy restoration. Its association with the tumor suppressor protein p53 is essential for controlling the growth of cells, apoptosis, and response to DNA damage. By raising p53 acetylation, encouraging apoptosis, and reducing cell proliferation, inhibiting SIRT1's catalytic domain, which interacts with p53, shows potential as a cancer treatment. The aim of the study is to find compounds that could inhibit SIRT1 and thus lower the proliferation of cancer cells. Employing molecular docking techniques, a virtual screening of ∼900 compounds (isolated from medicinal plants and derivatives) gave us 13 active compounds with good binding affinity. Additional evaluation of pharmacokinetic and pharmacodynamic properties led to the selection of eight compounds with desirable properties. Docking analysis confirmed stable interactions between the final eight compounds (C1-C8) and the SIRT1 catalytic domain. Molecular dynamics simulations show overall stability and moderate changes in protein structure upon compound binding. The compactness of the protein indicated the protein's tight packing upon the inhibitors binding. Binding free energy calculations revealed that compounds C2 (-49.96 ± 0.073 kcal/mol and C1 (-44.79 ± 0.077 kcal/mol) exhibited the highest energy, indicating strong binding affinity to the SIRT1 catalytic domain. These compounds, along with C8, C5, C6, C3, C4 and C7, showed promising potential as SIRT1 inhibitors. Based on their ability to reduce SIRT1 activity and increase apoptosis, the eight chemicals discovered in this work may be useful in treating cancer.Communicated by Ramaswamy H. Sarma.

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

The discovery of new scaffolds and chemotypes via high-throughput screening is tedious and resource intensive. Yet, there are millions of small molecules commercially available, rendering comprehensive in vitro tests intractable. We show how smart algorithms reduce large screening collections to target-specific sets of just a few hundred small molecules, allowing for a much faster and more cost-effective hit discovery process. We showcase the application of this virtual screening strategy by preselecting 434 compounds for Sirtuin-1 inhibition from a library of 2.6 million compounds, corresponding to 0.02% of the original library. Multistage in vitro validation ultimately confirmed nine chemically novel inhibitors. When compared to a competitive benchmark study for Sirtuin-1, our method shows a 12-fold higher hit rate. The results demonstrate how AI-driven preselection from large screening libraries allows for a massive reduction in the number of small molecules to be tested in vitro while still retaining a large number of hits.

Discovery of a Hidden Trypanosoma cruzi Spermidine Synthase Binding Site and Inhibitors through In Silico, In Vitro, and X-ray Crystallography

In drug discovery research, the selection of promising binding sites and understanding the binding mode of compounds are crucial fundamental studies. The current understanding of the proteins-ligand binding model extends beyond the simple lock and key model to include the induced-fit model, which alters the conformation to match the shape of the ligand, and the pre-existing equilibrium model, selectively binding structures with high binding affinity from a diverse ensemble of proteins. Although methods for detecting target protein binding sites and virtual screening techniques using docking simulation are well-established, with numerous studies reported, they only consider a very limited number of structures in the diverse ensemble of proteins, as these methods are applied to a single structure. Molecular dynamics (MD) simulation is a method for predicting protein dynamics and can detect potential ensembles of protein binding sites and hidden sites unobservable in a single-point structure. In this study, to demonstrate the utility of virtual screening with protein dynamics, MD simulations were performed on Trypanosoma cruzi spermidine synthase to obtain an ensemble of dominant binding sites with a high probability of existence. The structure of the binding site obtained through MD simulation revealed pockets in addition to the active site that was present in the initial structure. Using the obtained binding site structures, virtual screening of 4.8 million compounds by docking simulation, in vitro assays, and X-ray analysis was conducted, successfully identifying two hit compounds.

Quantitative comparison of protein-protein interaction interface using physicochemical feature-based descriptors of surface patches

Driving mechanisms of many biological functions in a cell include physical interactions of proteins. As protein-protein interactions (PPIs) are also important in disease development, protein-protein interactions are highlighted in the pharmaceutical industry as possible therapeutic targets in recent years. To understand the variety of protein-protein interactions in a proteome, it is essential to establish a method that can identify similarity and dissimilarity between protein-protein interactions for inferring the binding of similar molecules, including drugs and other proteins. In this study, we developed a novel method, protein-protein interaction-Surfer, which compares and quantifies similarity of local surface regions of protein-protein interactions. protein-protein interaction-Surfer represents a protein-protein interaction surface with overlapping surface patches, each of which is described with a three-dimensional Zernike descriptor (3DZD), a compact mathematical representation of 3D function. 3DZD captures both the 3D shape and physicochemical properties of the protein surface. The performance of protein-protein interaction-Surfer was benchmarked on datasets of protein-protein interactions, where we were able to show that protein-protein interaction-Surfer finds similar potential drug binding regions that do not share sequence and structure similarity. protein-protein interaction-Surfer is available at https://kiharalab.org/ppi-surfer.

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.

Screening for Inhibitors of Main Protease in SARS-CoV-2: In Silico and In Vitro Approach Avoiding Peptidyl Secondary Amides

In addition to vaccines, antiviral drugs are essential for suppressing COVID-19. Although several inhibitor candidates were reported for SARS-CoV-2 main protease, most are highly polar peptidomimetics with poor oral bioavailability and cell membrane permeability. Here, we conducted structure-based virtual screening and in vitro assays to obtain hit compounds belonging to a new chemical space, excluding peptidyl secondary amides. In total, 180 compounds were subjected to the primary assay at 20 μM, and nine compounds with inhibition rates of >5% were obtained. The IC50 of six compounds was determined in dose-response experiments, with the values on the order of 10-4 M. Although nitro groups were enriched in the substructure of the hit compounds, they did not significantly contribute to the binding interaction in the predicted docking poses. Physicochemical properties prediction showed good oral absorption. These new scaffolds are promising candidates for future optimization.

MERMAID: an open source automated hit-to-lead method based on deep reinforcement learning

The hit-to-lead process makes the physicochemical properties of the hit molecules that show the desired type of activity obtained in the screening assay more drug-like. Deep learning-based molecular generative models are expected to contribute to the hit-to-lead process. The simplified molecular input line entry system (SMILES), which is a string of alphanumeric characters representing the chemical structure of a molecule, is one of the most commonly used representations of molecules, and molecular generative models based on SMILES have achieved significant success. However, in contrast to molecular graphs, during the process of generation, SMILES are not considered as valid SMILES. Further, it is quite difficult to generate molecules starting from a certain molecule, thus making it difficult to apply SMILES to the hit-to-lead process. In this study, we have developed a SMILES-based generative model that can be generated starting from a certain molecule. This method generates partial SMILES and inserts it into the original SMILES using Monte Carlo Tree Search and a Recurrent Neural Network. We validated our method using a molecule dataset obtained from the ZINC database and successfully generated molecules that were both well optimized for the objectives of the quantitative estimate of drug-likeness (QED) and penalized octanol-water partition coefficient (PLogP) optimization. The source code is available at https://github.com/sekijima-lab/mermaid.

Computer aided drug discovery review for infectious diseases with case study of anti-Chagas project

Neglected tropical diseases (NTDs) are parasitic and bacterial infections that are widespread, especially in the tropics, and cause health problems for about one billion people over 149 countries worldwide. However, in terms of therapeutic agents, for example, nifurtimox and benznidazole were developed in the 1960s to treat Chagas disease, but new drugs are desirable because of their side effects. Drug discovery takes 12 to 14 years and costs $2.6 billon dollars, and hence, computer aided drug discovery (CADD) technology is expected to reduce the time and cost. This paper describes our methods and results based on CADD, mainly for NTDs. An overview of databases, molecular simulation and pharmacophore modeling, contest-based drug discovery, and machine learning and their results are presented herein.

Identification of key interactions between SARS-CoV-2 main protease and inhibitor drug candidates

The number of cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (COVID-19) has reached over 114,000. SARS-CoV-2 caused a pandemic in Wuhan, China, in December 2019 and is rapidly spreading globally. It has been reported that peptide-like anti-HIV-1 drugs are effective against SARS-CoV Main protease (Mpro). Due to the close phylogenetic relationship between SARS-CoV and SARS-CoV-2, their main proteases share many structural and functional features. Thus, these drugs are also regarded as potential drug candidates targeting SARS-CoV-2 Mpro. However, the mechanism of action of SARS-CoV-2 Mpro at the atomic-level is unknown. In the present study, we revealed key interactions between SARS-CoV-2 Mpro and three drug candidates by performing pharmacophore modeling and 1 μs molecular dynamics (MD) simulations. His41, Gly143, and Glu166 formed interactions with the functional groups that were common among peptide-like inhibitors in all MD simulations. These interactions are important targets for potential drugs against SARS-CoV-2 Mpro.