Approaching Pharmacological Space: Events and Components.

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.

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.

Artificial intelligence: Machine learning approach for screening large database and drug discovery

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

RNAs are a vast reservoir of untapped drug targets. Structure-based virtual screening (VS) identifies candidate molecules by leveraging binding site information, traditionally using molecular docking simulations. However, docking struggles to scale with large compound libraries and RNA targets. Machine learning offers a solution but remains underdeveloped for RNA due to limited data and practical evaluations. We introduce a data-driven VS pipeline tailored for RNA, utilizing coarse-grained 3D modeling, synthetic data augmentation, and RNA-specific self-supervision. Our model achieves a 10,000x speedup over docking while ranking active compounds in the top 2.8% on structurally distinct test sets. It is robust to binding site variations and successfully screens unseen RNA riboswitches in a 20,000-compound in-vitro microarray, with a mean enrichment factor of 2.93 at 1%. This marks the first experimentally validated success of structure-based deep learning for RNA VS.

Virtual screening is becoming an important tool for drug discovery. However, the application of virtual screening has been limited by the lack of accurate scoring functions. Here, we present a novel scoring function, MedusaScore, for evaluating protein-ligand binding. MedusaScore is based on models of physical interactions that include van der Waals, solvation, and hydrogen bonding energies. To ensure the best transferability of the scoring function, we do not use any protein-ligand experimental data for parameter training. We then test the MedusaScore for docking decoy recognition and binding affinity prediction and find superior performance compared to other widely used scoring functions. Statistical analysis indicates that one source of inaccuracy of MedusaScore may arise from the unaccounted entropic loss upon ligand binding, which suggests avenues of approach for further MedusaScore improvement.

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.

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.

The use of virtual screening and differential scanning fluorimetry for the rapid identification of fragments active against MEK1

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

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.

Discovery of MEK/PI3K dual inhibitor via structure-based virtual screening

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.

Virtual screening can be considered as anin silicoanalog to high throughput screening. Its goal is the drastic reduction of compound libraries to a manageable size for synthesis and biological testing. Virtual screening applications are not standardized and packaged in single software products. They rather consist of cascades of diverse filter programs that address different characteristics of putative lead chemical matter. Fast counting schemes for profiling physicochemical properties and machine learning approaches address druglike properties of small molecules related to pharmacology and toxicology. Focused libraries for target classes can be designed, for example, for combinatorial synthesis. Pharmacophores derived from known ligands of a given target can be used in virtual pharmacophore screening. Finally, structure‐based virtual screening employs docking and scoring approaches to find the binding mode of putative protein‐ligand complexes and estimate their binding affinities.