RNAmigos2: accelerated structure-based RNA virtual screening with deep graph learning

Objective: The objective of this study is to construct predictive unbiased structure-based virtual screening (SBVS) protocols to identify potent ligands for estrogen receptor alpha by combining molecular docking, protein-ligand interaction fingerprinting (PLIF), and binary quantitative structure-activity relationship (QSAR) analysis using recursive partition and regression tree method.Methods: Employing the enhanced version of a directory of useful decoys, SBVS protocols using molecular docking simulations, and PLIF were constructed and retrospectively validated. To avoid bias, SMILES format of the compounds was used. The predictive abilities of the SBVS protocols were then compared based on the enrichment factor (EF) and the F-measure values.Results: The SBVS protocols resulted in this research were SBVS_1 (employing docking scores of the best pose on every compound to rank the results and selecting compounds within 1% false positives as positive), SBVS_2 (employing decision tree resulted from the binary QSAR analysis using docking scores and PLIF bitstrings of the best pose of every compound as descriptors), and SBVS_3 (employing decision tree resulted from the binary QSAR analysis using ensemble PLIF of the selected poses from optimized docking score as the cutoff). The EF values of SBVS_1, SBVS_2, and SBVS_3 are 28.315, 576.084, and 713.472, respectively, while their F-measure values are 0.310, 0.573, and 0.769, respectively.Conclusion: Highly predictive unbiased SBVS protocols to identify potent estrogen receptor alpha ligands were constructed. Further application in prospective screening is therefore highly suggested.

Bromodomain-containing protein 4 (BRD4) is implicated in the pathogenesis of a number of different cancers, inflammatory diseases and heart failure. Much effort has been dedicated toward discovering novel scaffold BRD4 inhibitors (BRD4is) with different selectivity profiles and potential antiresistance properties. Structure-based drug design (SBDD) and virtual screening (VS) are the most frequently used approaches. Here, we demonstrate a novel, structure-based VS approach that uses machine-learning algorithms trained on the priori structure and activity knowledge to predict the likelihood that a compound is a BRD4i based on its binding pattern with BRD4. In addition to positive experimental data, such as X-ray structures of BRD4-ligand complexes and BRD4 inhibitory potencies, negative data such as false positives (FPs) identified from our earlier ligand screening results were incorporated into our knowledge base. We used the resulting data to train a machine-learning model named BRD4LGR to predict the BRD4i-likeness of a compound. BRD4LGR achieved a 20-30% higher AUC-ROC than that of Glide using the same test set. When conducting in vitro experiments against a library of previously untested, commercially available organic compounds, the second round of VS using BRD4LGR generated 15 new BRD4is. Moreover, inverting the machine-learning model provided easy access to structure-activity relationship (SAR) interpretation for hit-to-lead optimization.

Integrated in silico-in vitro strategy for screening of some traditional Egyptian plants for human aromatase inhibitors

The drug development process is a major challenge in the pharmaceutical industry since it takes a substantial amount of time and money to move through all the phases of developing of a new drug. One extensively used method to minimize the cost and time for the drug development process is computer-aided drug design (CADD). CADD allows better focusing on experiments, which can reduce the time and cost involved in researching new drugs. In this context, structure-based virtual screening (SBVS) is robust and useful and is one of the most promising in silico techniques for drug design. SBVS attempts to predict the best interaction mode between two molecules to form a stable complex, and it uses scoring functions to estimate the force of non-covalent interactions between a ligand and molecular target. Thus, scoring functions are the main reason for the success or failure of SBVS software. Many software programs are used to perform SBVS, and since they use different algorithms, it is possible to obtain different results from different software using the same input. In the last decade, a new technique of SBVS called consensus virtual screening (CVS) has been used in some studies to increase the accuracy of SBVS and to reduce the false positives obtained in these experiments. An indispensable condition to be able to utilize SBVS is the availability of a 3D structure of the target protein. Some virtual databases, such as the Protein Data Bank, have been created to store the 3D structures of molecules. However, sometimes it is not possible to experimentally obtain the 3D structure. In this situation, the homology modeling methodology allows the prediction of the 3D structure of a protein from its amino acid sequence. This review presents an overview of the challenges involved in the use of CADD to perform SBVS, the areas where CADD tools support SBVS, a comparison between the most commonly used tools, and the techniques currently used in an attempt to reduce the time and cost in the drug development process. Finally, the final considerations demonstrate the importance of using SBVS in the drug development process.

Neuropathic pain is due to an injury or disease of the somatosensory nervous system, which accounts for a significant economical and health burden to society. Due to poor understanding of their underlying mechanisms, the available treatments merely provide symptomatic relief and precipitates a variety of adverse effects. This suggests that there is an unmet medical need that must be addressed with effective strategies for the development of novel therapeutics. Sphingosine kinase 2 (SphK2) is an oncogenic lipid kinase that has emerged as a promising target for chronic pain and other diseases. In the present study, we have explored the structure-based virtual high-throughput screening of the Nuclei of Bioassays, Ecophysiology, and Biosynthesis of Natural Products Database (NuBBE) to identify potent natural products as inhibitors of SphK2. A molecular docking study was performed to calculate binding affinities and specificity to identify potential leads against SphK2. Initially, hits were selected by the implementation of absorption, distribution, metabolism, excretion and toxicity properties, Lipinski rule, and PAINS filters. The top-scoring hits also exhibiting an optimal ADMET profile were subjected to MM/GBSA free binding free energy calculation and molecular dynamics simulation. The results from molecular dynamics simulation revealed a stable ligand -SphK2 complex with protein and ligand RMSD within reasonable limits. Overall, we identified compounds, NuBBE_972 and NuBBE_1107 as potential inhibitors of SphK2 with optimal pharmacokinetic properties which have the potential to be developed as novel therapeutics for the management of chronic pain. Communicated by Ramaswamy H. Sarma

Structure-based drug discovery (SBDD) is becoming an essential tool in assisting fast and cost-efficient leaddiscovery and optimization. The application of rational, structure-based drug design is proven to be more efficient than thetraditional way of drug discovery since it aims to understand the molecular basis of a disease and utilizes the knowledgeof the three-dimensional structure of the biological target in the process. In this review, we focus on the principles and applicationsof Virtual Screening (VS) within the context of SBDD and examine different procedures ranging from the initialstages of the process that include receptor and library pre-processing, to docking, scoring and post-processing of topscoringhits. Recent improvements in structure-based virtual screening (SBVS) efficiency through ensemble docking, inducedfit and consensus docking are also discussed. The review highlights advances in the field within the framework ofseveral success studies that have led to nM inhibition directly from VS and provides recent trends in library design as wellas discusses limitations of the method. Applications of SBVS in the design of substrates for engineered proteins that enablethe discovery of new metabolic and signal transduction pathways and the design of inhibitors of multifunctional proteinsare also reviewed. Finally, we contribute two promising VS protocols recently developed by us that aim to increaseinhibitor selectivity. In the first protocol, we describe the discovery of micromolar inhibitors through SBVS designed toinhibit the mutant H1047R PI3Kα kinase. Second, we discuss a strategy for the identification of selective binders for theRXRα nuclear receptor. In this protocol, a set of target structures is constructed for ensemble docking based on bindingsite shape characterization and clustering, aiming to enhance the hit rate of selective inhibitors for the desired protein targetthrough the SBVS process.

Cancer continues to remain a disease of scientific concern. Significant interest in targeting the Polo-Box-Domain (PBD) of Polo-like-kinase-1 (PLK-1) by novel ligands has arisen. The 'cleft' constituted by amino acid residues W414, H538, and K540 is the traditional target of PLK-1-PBD-inhibitors. However, this 'cleft' is merely a small part of the larger 'Y'-shaped cavity present therein. The objective of this study was to discover inhibitors of the PLK-1-PBD precisely directed against its trimodular 'Y'-pocket. High-throughput structure-based virtual screening (SBVS) of more than 5 million ligands against the aforementioned PLK-1 'Y'-pocket was performed. The SBVS hits were successively subjected to pass through various filters: VINA score ranking, toxicity checker, 'Special Criteria'-filtration, holistic tri-modular 'Y'-pocket interaction check, drug-likeness filters, and medicinal chemistry filters. Accordingly, we arrived at a single top ligand, 'SHAZ-i.' The top ligand, 3-{2-[(2-Methyl-2-propanyl)sulfonyl]phenyl}-5-phenyl-1,2-oxazole-4-carboxamide, displayed a robust interaction with the target crevice through 15 amino acid residues, an acceptable ΔG value of -7.8 kcal/mol, and a favorable pharmacokinetic profile with no adverse effects on humans. Hence, 3-{2-[(2-Methyl-2-propanyl)sulfonyl]phenyl}-5-phenyl-1,2-oxazole-4-carboxamide could emerge as a potent PLK-1-PBD inhibitor or might act as a 'seed' molecule for design of future inhibitors with a closely related backbone structure.

The aim of this study was to investigate the usefulness of structure-based virtual screening (VS) for focused library design in G protein-coupled receptors (GPCR) projects on the example of 5-HT(2c) agonists. We compared the performance of structure-based VS against two different homology models using FRED for docking and ScreenScore, FlexX, and PMF for rescoring with the results of 12 ligand-based similarity searches using four different query compounds and three different similarity metrics (Daylight, FTree, Phacir). The result of the similarity search showed much variation, from an enrichment factor up to 3.2 to worse than random, whereas the structure-based VS gave a more stable result with a constant enrichment factor around 2. Additionally, actives retrieved by the structure-based approach were more diverse than the actives among the top scorers of the similarity searches. Based on these results, we suggest basing a focused library design for a GPCR project on a combination of a ligand-based similarity search and structure-based docking.

Considerations of binding pocket dynamics are one of the crucial aspects of the rational design of binders. Identification of alternative conformational states or cryptic subpockets could lead to the discovery of completely novel groups of the ligands. However, experimental characterization of pocket dynamics, besides being expensive, may not be able to elucidate all of the conformational states relevant for drug discovery projects. In this study, we propose the protocol for computational simulations of sirtuin 2 (SIRT2) binding pocket dynamics and its integration into the structure-based virtual screening (SBVS) pipeline. Initially, unbiased molecular dynamics simulations of SIRT2:inhibitor complexes were performed using optimized force field parameters of SIRT2 inhibitors. Time-lagged independent component analysis (tICA) was used to design pocket-related collective variables (CVs) for enhanced sampling of SIRT2 pocket dynamics. Metadynamics simulations in the tICA eigenvector space revealed alternative conformational states of the SIRT2 binding pocket and the existence of a cryptic subpocket. Newly identified SIRT2 conformational states outperformed experimentally resolved states in retrospective SBVS validation. After performing prospective SBVS, compounds from the under-represented portions of the SIRT2 inhibitor chemical space were selected for in vitro evaluation. Two compounds, NDJ18 and NDJ85, were identified as potent and selective SIRT2 inhibitors, which validated the in silico protocol and opened up the possibility for generalization and broadening of its application. The anticancer effects of the most potent compound NDJ18 were examined on the triple-negative breast cancer cell line. Results indicated that NDJ18 represents a promising structure suitable for further evaluation.

Ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS) approaches were used to identify new inhibitors for ATAD2 bromodomain. The LBVS approach was used to search 23,129,083 clean compounds to identify compounds similar to an active compound with reported pIC50 equal to 7.2. Based on LBVS results, 19 compounds were selected. To perform SBVS, by applying nine filters on 23,129,083 clean compounds, 1,057,060 compounds were selected. After performing SBVS on these selected compounds with idock software, 16 compounds with the lowest binding energies were selected. More accurate molecular docking analysis was performed on these 35 selected compounds by using iGEMDOCK software and six of them with the lowest binding energies were selected as hit compounds. These compounds were zinc36647229, zinc77969074, zinc13637358, zinc77971540, zinc12991296 and zinc19374204.

Targeting OPA1 protein for therapeutic intervention in autosomal dominant optic atrophy: In silico drug discovery.

Pancreatic cancer is an abnormal cell growth in the pancreas. In 2021, approximately 60,430 individuals were diagnosed in the USA, with the annual increasing incidence rates. Pancreatic cancer is anticipated to become the second leading cause of cancer mortality by 2030. This escalating challenge has prompted a search for innovative therapeutic agents. Virtual screening, a computational technique, was employed to discover novel drug-like compounds from a diverse set of 30 chemical compounds, sourced from the PubChem database. These compounds were evaluated based on some important properties, including pharmacokinetics, lipophilicity, drug-likeness, water-solubility, and physicochemical characteristics. Seventeen compounds emerged as promising candidates for pancreatic cancer treatment. Subsequent molecular docking studies focused on the Kras-G12D protein target and identified Ligand 18 as the leading candidate, exhibiting a binding energy (BE) of -10.5 kcal mol-1 and extensive interactions with the target protein. Additionally, a newly designed compound, D4, displayed an even higher BE of -10.8 kcal mol-1, fitting more effectively into the protein's binding site than existing drugs like Gemcitabine and Irinotecan. All newly designed compounds met the five scientists' rule, indicating favorable drug-likeness and bioavailability. These findings pave the way for developing a new generation of less toxic therapeutic compounds for pancreatic cancer treatment.Keywords: virtual screening, binding energy, kras-G12D, pancreatic cancer, designed compounds.

Drug design and development are crucial areas of study for chemists and pharmaceutical companies. Nevertheless, the significant expenses, lengthy process, inaccurate delivery, and limited effectiveness present obstacles and barriers that affect the development and exploration of new drugs. Moreover, big and complex datasets from clinical trials, genomics, proteomics, and microarray data also disrupt the drug discovery approach. The integration of Artificial Intelligence (AI) into drug design is both timely and crucial due to several pressing challenges in the pharmaceutical industry, including the escalating costs of drug development, high failure rates in clinical trials, and the increasing complexity of disease biology. AI offers innovative solutions to address these challenges, promising to improve the efficiency, precision, and success rates of drug discovery and development. Artificial intelligence (AI) and machine learning (ML) technology are crucial tools in the field of drug discovery and development. More precisely, the field has been revolutionized by the utilization of deep learning (DL) techniques and artificial neural networks (ANNs). DL algorithms & ML have been employed in drug design using various approaches such as physiochemical activity, polypharmacology, drug repositioning, quantitative structure-activity relationship, pharmacophore modeling, drug monitoring and release, toxicity prediction, ligand-based virtual screening, structure-based virtual screening, and peptide synthesis. The use of DL and AI in this field is supported by historical evidence. Furthermore, management strategies, curation, and unconventional data mining aided assistance in modern modeling algorithms. In summary, the progress made in artificial intelligence and deep learning algorithms offers a promising opportunity for the development and discovery of effective drugs, ultimately leading to significant benefits for humanity. In this review, several tools and algorithmic programs have been discussed which are being used in drug design along with the descriptions of the patents that have been granted for the use of AI in this field, which constitutes the main focus of this review and differentiates it fromalready published materials.

Insect molting is regulated by 20-hydroxyecdsysone (20E), and the disruption of the molting process leads to death. To date, non-steroidal ecdysone agonists, diacylhydrazine (DAH)-type compounds, have been used as insecticides. We will review classical quantitative structure activity relationship (QSAR) studies for the in vivo and in vitro activity of DAHs and ecdysteroids. In the QSAR study for ecdysteroids, we applied three-dimensional QSAR, where hydrogen bond number was used as a parameter. The ligand-receptor complex was constructed by the homology modeling technique and the number of hydrogen bonds was counted. In recent drug design, computational approaches such as virtual (in silico) screening (VS) and high throughput screening (HTS) are often used. The VS procedure is classified as ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS); both are often used for the design of pharmaceuticals. We applied LBVS to find novel ecdysone agonists, in which ponasterone A (PoA), the most potent ecdysteroid, was used as a template molecule. Other computational techniques such as homology modeling and bioinformatics are also briefly reviewed.

This review provides a comprehensive analysis of the transformative impact of artificial intelligence (AI) and machine learning (ML) on modern drug design, specifically focusing on how these advanced computational techniques address the inherent limitations of traditional small-molecule drug design methodologies. It begins by outlining the historical challenges of the drug discovery pipeline, including protracted timelines, exorbitant costs, and high clinical failure rates. Subsequently, it examines the core principles of structure-based virtual screening (SBVS) and ligand-based virtual screening (LBVS), establishing the critical bottlenecks that have historically impeded efficient drug development. The central sections elucidate how cutting-edge ML and deep learning (DL) paradigms, such as generative models and reinforcement learning, are revolutionizing chemical space exploration, enhancing binding affinity prediction, improving protein flexibility modeling, and automating critical design tasks. Illustrative real-world case studies demonstrating quantifiable accelerations in discovery timelines and improved success probabilities are presented. Finally, the review critically examines prevailing challenges, including data quality, model interpretability, ethical considerations, and evolving regulatory landscapes, while offering forward-looking critical perspectives on the future trajectory of AI-driven pharmaceutical innovation.