Virtual screening—an overview

Docking with AutoDock4

AutoDock is one of the most popular receptor-ligand docking simulation programs. It was first released in the early 1990s and is in continuous development and adapted to specific protein targets. AutoDock has been applied to a wide range of biological systems. It has been used not only for protein-ligand docking simulation but also for the prediction of binding affinity with good correlation with experimental binding affinity for several protein systems. The latest version makes use of a semi-empirical force field to evaluate protein-ligand binding affinity and for selecting the lowest energy pose in docking simulation. AutoDock4.2.6 has an arsenal of four search algorithms to carry out docking simulation including simulated annealing, genetic algorithm, and Lamarckian algorithm. In this chapter, we describe a tutorial about how to perform docking with AutoDock4. We focus our simulations on the protein target cyclin-dependent kinase 2.

Rigorous Free Energy Simulations in Virtual Screening

Virtual high throughput screening (vHTS) in drug discovery is a powerful approach to identify hits: when applied successfully, it can be much faster and cheaper than experimental high-throughput screening approaches. However, mainstream vHTS tools have significant limitations: ligand-based methods depend on knowledge of existing chemical matter, while structure-based tools such as docking involve significant approximations that limit their accuracy. Recent advances in scientific methods coupled with dramatic speedups in computational processing with GPUs make this an opportune time to consider the role of more rigorous methods that could improve the predictive power of vHTS workflows. In this Perspective, we assert that alchemical binding free energy methods using all-atom molecular dynamics simulations have matured to the point where they can be applied in virtual screening campaigns as a final scoring stage to prioritize the top molecules for experimental testing. Specifically, we propose that alchemical absolute binding free energy (ABFE) calculations offer the most direct and computationally efficient approach within a rigorous statistical thermodynamic framework for computing binding energies of diverse molecules, as is required for virtual screening. ABFE calculations are particularly attractive for drug discovery at this point in time, where the confluence of large-scale genomics data and insights from chemical biology have unveiled a large number of promising disease targets for which no small molecule binders are known, precluding ligand-based approaches, and where traditional docking approaches have foundered to find progressible chemical matter.

Druggability and drug-likeness concepts in drug design: are biomodelling and predictive tools having their say?

The drug discovery process typically involves target identification and design of suitable drug molecules against these targets. Despite decades of experimental investigations in the drug discovery domain, about 96% overall failure rate has been recorded in drug development due to the "undruggability" of various identified disease targets, in addition to other challenges. Likewise, the high attrition rate of drug candidates in the drug discovery process has also become an enormous challenge for the pharmaceutical industry. To alleviate this negative outlook, new trends in drug discovery have emerged. By drifting away from experimental research methods, computational tools and big data are becoming valuable in the prediction of biological target druggability and the drug-likeness of potential therapeutic agents. These tools have proven to be useful in saving time and reducing research costs. As with any emerging technique, however, controversial opinions have been presented regarding the validation of predictive computational tools. To address the challenges associated with these varying opinions, this review attempts to highlight the principles of druggability and drug-likeness and their recent advancements in the drug discovery field. Herein, we present the different computational tools and their reliability of predictive analysis in the drug discovery domain. We believe that this report would serve as a comprehensive guide towards computational-oriented drug discovery research. Graphical abstract Highlights of methods for assessing the druggability of biological targets.

Developing an effective polarizable bond method for small molecules with application to optimized molecular docking

Electrostatic interaction plays an essential role in protein-ligand binding. Due to the polarization effect, electrostatic interactions are largely impacted by their local environments. However, traditional force fields use fixed point charge-charge interactions to describe electrostatic interactions but is unable to include the polarization effect. The lack of the polarization effect in the force field representation can result in substantial error in biomolecular studies, such as molecular dynamics and molecular docking. Docking programs usually employ traditional force fields to estimate the binding energy between a ligand and a protein for pose selection or scoring. The intermolecular interaction energy mainly consists of van der Waals and electrostatic interaction in the force field representation. In the current study, we developed an Effective Polarizable Bond (EPB) method for small organic molecules and applied this EPB method to optimize protein-ligand docking in computational tests for a variety of protein-ligand systems. We tested the method on a set of 38 cocrystallized structures taken from the Protein Data Bank (PDB) and found that the maximum error was reduced from 7.98 Å to 2.03 Å when using EPB Dock, providing strong evidence that the use of EPB charges is important. We found that our optimized docking approach with EPB charges could improve the docking performance, sometimes dramatically, and the maximum error was reduced from 12.88 Å to 1.57 Å in Optimized Docking (in the case of 1fqx). The average RMSD decreased from 2.83 Å to 1.85 Å. Further investigations showed that the use of the EBP method could enhance intermolecular hydrogen bonding, which is a major contributing factor to improved docking performance. Developed tools for the calculation of the polarized ligand charge from a protein-ligand complex structure with the EPB method are freely available on GitHub (https://github.com/Xundrug/EPB).

The power of deep learning to ligand-based novel drug discovery

INTRODUCTION

Deep discriminative and generative neural-network models are becoming an integral part of the modern approach to ligand-based novel drug discovery. The variety of different architectures of neural networks, the methods of their training, and the procedures of generating new molecules require expert knowledge to choose the most suitable approach.

AREAS COVERED

Three different approaches to deep learning use in ligand-based drug discovery are considered: virtual screening, neural generative models, and mutation-based structure generation. Several architectures of neural networks for building either discriminative or generative models are considered in this paper, including deep multilayer neural networks, different kinds of convolutional neural networks, recurrent neural networks, and several types of autoencoders. Several kinds of learning frameworks are also considered, including adversarial learning and reinforcement learning. Different types of representations for generating molecules, including SMILES, graphs, and several alternative string representations are also considered.

EXPERT OPINION

Two kinds of problem should be solved in order to make the models built using deep neural networks, especially generative models, a valuable option in ligand-based drug discovery: the issue of interpretability and explainability of deep-learning models and the issue of synthetic accessibility of novel compounds designed by deep-learning algorithms.

Discovery of novel and potent P2Y14R antagonists via structure-based virtual screening for the treatment of acute gouty arthritis

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A reliable Glide docking-based virtual screening (VS) pipeline for P2Y₁₄R was developed.

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Several potent P2Y₁₄R antagonists with novel scaffolds were identified utilizing the VS strategy.

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P2Y₁₄R inhibitory effect was evaluated by testing cAMP levels in HEK293 cells.

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Anti-gout activity of screened compound was detected in MSU-treated THP-1 cells.

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The mechanism of test compound in treating acute gouty arthritis was elucidated.

P2Y₁₄ nucleotide receptor is a Gi protein-coupled receptor, which is widely involved in physiological and pathologic events. Although several P2Y₁₄R antagonists have been developed thus far, few have successfully been developed into a therapeutic drug. In this study, on the basis of two P2Y₁₄R homology models, Glide docking-based virtual screening (VS) strategy was employed for finding potent P2Y₁₄R antagonists with novel chemical architectures. A total of 19 structurally diverse compounds identified by VS and drug-like properties testing were set to experimental testing. 10 of them showed good inhibitory effects against the P2Y₁₄R (IC₅₀ < 50 nM), including four compounds (compounds 8, 10, 18 and 19) with IC₅₀ value below 10 nM. The best VS hit, compound 8 exhibited the best antagonistic activity, with IC₅₀ value of 2.46 nM. More importantly, compound 8 restrained monosodium uric acid (MSU)-induced pyroptosis of THP-1 cells through blocking the activation of Nod-like receptor 3 (NLRP3) inflammasome, which was attributed to its inhibitory effects on P2Y₁₄R-cAMP pathways. The key favorable residues uncovered using MM/GBSA binding free energy calculations/decompositions were detected and discussed. These findings suggest that the compound 8 can be used as a good lead compound for further optimization to obtain more promising P2Y₁₄R antagonists for the treatment of acute gouty arthritis.

Hops compounds modulatory effects and 6-prenylnaringenin dual mode of action on GABAA receptors

Hops (Humulus lupulus L.), a major component of beer, contain potentially neuroactive compounds that made it useful in traditional medicine as a sleeping aid. The present study aims to investigate the individual components in hops acting as allosteric modulators in GABA_A receptors and bring further insight into the mode of action behind the sedative properties of hops. GABA-potentiating effects were measured using [³H]ethynylbicycloorthobenzoate (EBOB) radioligand binding assay in native GABA_A receptors. Flumazenil sensitivity of GABA-potentiating effects, [³H]Ro 15-4513, and [³H]flunitrazepam binding assays were used to examine the binding to the classical benzodiazepines site. Humulone (alpha acid) and 6-prenylnaringenin (prenylflavonoid) were the most potent compounds displaying a modulatory activity at low micromolar concentrations. Humulone and 6-prenylnaringenin potentiated GABA-induced displacement of [³H]EBOB binding in a concentration-dependent manner where the IC₅₀ values for this potentiation in native GABA_A receptors were 3.2 μM and 3.7 μM, respectively. Flumazenil had no significant effects on humulone- or 6-prenylnaringenin-induced displacement of [³H]EBOB binding. [³H]Ro 15-4513 and [³H]flunitrazepam displacements were only minor with humulone but surprisingly prominent with 6-prenylnaringenin despite its flumazenil-insensitive modulatory activity. Thus, we applied molecular docking methods to investigate putative binding sites and poses of 6-prenylnaringenin at the GABA_A receptor α1β2γ2 isoform. Radioligand binding and docking results suggest a dual mode of action by 6-prenylnaringenin on GABA_A receptors where it may act as a positive allosteric modulator at α+β- binding interface as well as a null modulator at the flumazenil-sensitive α+γ2- binding interface.

Development of Natural Compound Molecular Fingerprint (NC-MFP) with the Dictionary of Natural Products (DNP) for natural product-based drug development

Computer-aided research on the relationship between molecular structures of natural compounds (NC) and their biological activities have been carried out extensively because the molecular structures of new drug candidates are usually analogous to or derived from the molecular structures of NC. In order to express the relationship physically realistically using a computer, it is essential to have a molecular descriptor set that can adequately represent the characteristics of the molecular structures belonging to the NC’s chemical space. Although several topological descriptors have been developed to describe the physical, chemical, and biological properties of organic molecules, especially synthetic compounds, and have been widely used for drug discovery researches, these descriptors have limitations in expressing NC-specific molecular structures. To overcome this, we developed a novel molecular fingerprint, called Natural Compound Molecular Fingerprints (NC-MFP), for explaining NC structures related to biological activities and for applying the same for the natural product (NP)-based drug development. NC-MFP was developed to reflect the structural characteristics of NCs and the commonly used NP classification system. NC-MFP is a scaffold-based molecular fingerprint method comprising scaffolds, scaffold-fragment connection points (SFCP), and fragments. The scaffolds of the NC-MFP have a hierarchical structure. In this study, we introduce 16 structural classes of NPs in the Dictionary of Natural Product database (DNP), and the hierarchical scaffolds of each class were calculated using the Bemis and Murko (BM) method. The scaffold library in NC-MFP comprises 676 scaffolds. To compare how well the NC-MFP represents the structural features of NCs compared to the molecular fingerprints that have been widely used for organic molecular representation, two kinds of binary classification tasks were performed. Task I is a binary classification of the NCs in commercially available library DB into a NC or synthetic compound. Task II is classifying whether NCs with inhibitory activity in seven biological target proteins are active or inactive. Two tasks were developed with some molecular fingerprints, including NC-MFP, using the 1-nearest neighbor (1-NN) method. The performance of task I showed that NC-MFP is a practical molecular fingerprint to classify NC structures from the data set compared with other molecular fingerprints. Performance of task II with NC-MFP outperformed compared with other molecular fingerprints, suggesting that the NC-MFP is useful to explain NC structures related to biological activities. In conclusion, NC-MFP is a robust molecular fingerprint in classifying NC structures and explaining the biological activities of NC structures. Therefore, we suggest NC-MFP as a potent molecular descriptor of the virtual screening of NC for natural product-based drug development.

Bidirectional Molecule Generation with Recurrent Neural Networks

Recurrent neural networks (RNNs) are able to generate de novo molecular designs using simplified molecular input line entry systems (SMILES) string representations of the chemical structure. RNN-based structure generation is usually performed unidirectionally, by growing SMILES strings from left to right. However, there is no natural start or end of a small molecule, and SMILES strings are intrinsically nonunivocal representations of molecular graphs. These properties motivate bidirectional structure generation. Here, bidirectional generative RNNs for SMILES-based molecule design are introduced. To this end, two established bidirectional methods were implemented, and a new method for SMILES string generation and data augmentation is introduced-the bidirectional molecule design by alternate learning (BIMODAL). These three bidirectional strategies were compared to the unidirectional forward RNN approach for SMILES string generation, in terms of the (i) novelty, (ii) scaffold diversity, and (iii) chemical-biological relevance of the computer-generated molecules. The results positively advocate bidirectional strategies for SMILES-based molecular de novo design, with BIMODAL showing superior results to the unidirectional forward RNN for most of the criteria in the tested conditions. The code of the methods and the pretrained models can be found at URL https://github.com/ETHmodlab/BIMODAL.

Development of a Novel Cell-Permeable Protein-Protein Interaction Inhibitor for the Polo-box Domain of Polo-like Kinase 1

Polo-like kinase 1 (PLK1) is a key regulator of mitosis and a recognized drug target for cancer therapy. Inhibiting the polo-box domain of PLK1 offers potential advantages of increased selectivity and subsequently reduced toxicity compared with targeting the kinase domain. However, many if not all existing polo-box domain inhibitors have been shown to be unsuitable for further development. In this paper, we describe a novel compound series, which inhibits the protein-protein interactions of PLK1 via the polo-box domain. We combine high throughput screening with molecular modeling and computer-aided design, synthetic chemistry, and cell biology to address some of the common problems with protein-protein interaction inhibitors, such as solubility and potency. We use molecular modeling to improve the solubility of a hit series with initially poor physicochemical properties, enabling biophysical and biochemical characterization. We isolate and characterize enantiomers to improve potency and demonstrate on-target activity in both cell-free and cell-based assays, entirely consistent with the proposed binding model. The resulting compound series represents a promising starting point for further progression along the drug discovery pipeline and a new tool compound to study kinase-independent PLK functions.

Latest trends in structure based drug design with protein targets

Structure based drug designing is an important endeavor in the field of structural bioinformatics. Previously the entire process was dependent on the wet-lab experiments to build libraries of ligand molecules. And the molecules used to be tested to determine their binding efficacies with protein target. However, the entire process is very lengthy and above all highly expensive. With the advent of supercomputers and increasing computational powers, the search process for finding suitable ligand molecules against target proteins have become more streamlined and cost-effective. Now the entire ligand search process is performed in-silico with the help of the techniques of virtual screening, molecular docking simulations and molecular dynamics studies. In the present chapter, a brief overview of the computational techniques involved in structure based drug designing is presented with a special emphasis on the thermodynamic principles behind the molecular interactions.

Efficient molecular encoders for virtual screening

Molecular representations encoding molecular structure information play critical roles in molecular virtual screening (VS). In order to improve VS performance, an abundance of molecular encoders have been developed and tested by various VS challenges. Combinational strategies were also used to improve the performance. Deep learning (DL)-based molecular encoders have attracted much attention for their automatic information extraction ability. In this review, we present an overview of two-dimensional-, three-dimensional-, and DL-based molecular encoders, summarize recent progress of VS using DL technologies, and propose a general framework of DL molecular encoder-based VS. Perspectives on the future directions of molecular representations and applications in the prediction of active compounds are also provided.

Development of Predictive Models for Identifying Potential S100A9 Inhibitors Based on Machine Learning Methods

S100A9 is a potential therapeutic target for various disease including prostate cancer, colorectal cancer, and Alzheimer's disease. However, the sparsity of atomic level data, such as protein-protein interaction of S100A9 with RAGE, TLR4/MD2, or CD147 (EMMPRIN) hinders the rational drug design of S100A9 inhibitors. Herein we first report predictive models of S100A9 inhibitory effect by applying machine learning classifiers on 2D-molecular descriptors. The models were optimized through feature selectors as well as classifiers to produce the top eight random forest models with robust predictability and high cost-effectiveness. Notably, optimal feature sets were obtained after the reduction of 2,798 features into dozens of features with the chopping of fingerprint bits. Moreover, the high efficiency of compact feature sets allowed us to further screen a large-scale dataset (over 6,000,000 compounds) within a week. Through a consensus vote of the top models, 46 hits (hit rate = 0.000713%) were identified as potential S100A9 inhibitors. We expect that our models will facilitate the drug discovery process by providing high predictive power as well as cost-reduction ability and give insights into designing novel drugs targeting S100A9.

The In Silico Drug Discovery Toolbox: Applications in Lead Discovery and Optimization

BACKGROUND

Discovery and development of a new drug is a long lasting and expensive journey that takes around 20 years from starting idea to approval and marketing of new medication. Despite R&D expenditures have been constantly increasing in the last few years, the number of new drugs introduced into market has been steadily declining. This is mainly due to preclinical and clinical safety issues, which still represent about 40% of drug discontinuation. To cope with this issue, a number of in silico techniques are currently being used for an early stage evaluation/prediction of potential safety issues, allowing to increase the drug-discovery success rate and reduce costs associated with the development of a new drug.

METHODS

In the present review, we will analyse the early steps of the drug-discovery pipeline, describing the sequence of steps from disease selection to lead optimization and focusing on the most common in silico tools used to assess attrition risks and build a mitigation plan.

RESULTS

A comprehensive list of widely used in silico tools, databases, and public initiatives that can be effectively implemented and used in the drug discovery pipeline has been provided. A few examples of how these tools can be problem-solving and how they may increase the success rate of a drug discovery and development program have been also provided. Finally, selected examples where the application of in silico tools had effectively contributed to the development of marketed drugs or clinical candidates will be given.

CONCLUSION

The in silico toolbox finds great application in every step of early drug discovery: (i) target identification and validation; (ii) hit identification; (iii) hit-to-lead; and (iv) lead optimization. Each of these steps has been described in details, providing a useful overview on the role played by in silico tools in the decision-making process to speed-up the discovery of new drugs.

The Symbiotic Relationship Between Drug Discovery and Organic Chemistry

All pharmaceutical products contain organic molecules; the source may be a natural product or a fully synthetic molecule, or a combination of both. Thus, it follows that organic chemistry underpins both existing and upcoming pharmaceutical products. The reverse relationship has also affected organic synthesis, changing its landscape towards increasingly complex targets. This Review article sets out to give a concise appraisal of this symbiotic relationship between organic chemistry and drug discovery, along with a discussion of the design concepts and highlighting key milestones along the journey. In particular, criteria for a high-quality compound library design enabling efficient virtual navigation of chemical space, as well as rise and fall of concepts for its synthetic exploration (such as combinatorial chemistry; diversity-, biology-, lead-, or fragment-oriented syntheses; and DNA-encoded libraries) are critically surveyed.

Efficient multi-objective molecular optimization in a continuous latent space

One of the main challenges in small molecule drug discovery is finding novel chemical compounds with desirable properties. In this work, we propose a novel method that combines in silico prediction of molecular properties such as biological activity or pharmacokinetics with an in silico optimization algorithm, namely Particle Swarm Optimization. Our method takes a starting compound as input and proposes new molecules with more desirable (predicted) properties. It navigates a machine-learned continuous representation of a drug-like chemical space guided by a defined objective function. The objective function combines multiple in silico prediction models, defined desirability ranges and substructure constraints. We demonstrate that our proposed method is able to consistently find more desirable molecules for the studied tasks in relatively short time. We hope that our method can support medicinal chemists in accelerating and improving the lead optimization process.

Discovery of novel natural compound inhibitors targeting estrogen receptor α by an integrated virtual screening strategy

Estrogen receptor (ER) is a nuclear hormone receptor and plays an important role in mediating the cellular effects of estrogen. ER can be classified into two receptors: estrogen receptor alpha (ERα) and beta (ERβ), and the former is expressed in 50~80% of breast tumors and has been extensively investigated in breast cancer for decades. Excessive exposure to estrogen can obviously stimulate the growth of breast cancers primarily mediated by ERα, and thus anti-estrogen therapies by small molecules are of concern to clinicians and pharmaceutical industry in the treatment of ERα-positive breast cancers. Although a series of estrogen receptor modulators have been developed, these drugs can lead to resistance and side effects. Therefore, the development of small molecule inhibitors with high target specificity has been intensified. In this pursuit, an integrated computer-aided virtual screening technique, including molecular docking and pharmacophore model screening, was used to screen traditional Chinese medicine (TCM) databases. The compounds with high docking scores and fit values were subjected to ADME (adsorption, distribution, metabolism, excretion) and toxicity prediction, and ten hits were identified as potential inhibitors targeting ERα. Molecular docking was used to investigate the binding modes between ERα and three most potent hits, and molecular dynamic simulations were chosen to explore the stability of these complexes. The rank of the predicted binding free energies evaluated by MM/GBSA is consistent with the docking score. These novel scaffolds discovered in the present study can be used as critical starting point in the drug discovery process for treating ERα-positive breast cancer. Graphical abstract .

Identification of Novel Allosteric Modulators of Metabotropic Glutamate Receptor Subtype 5 Acting at Site Distinct from 2-Methyl-6-(phenylethynyl)-pyridine Binding

As part of the G-protein coupled receptor (GPCR) family, metabotropic glutamate (mGlu) receptors play an important role as drug targets of cognitive diseases. Selective allosteric modulators of mGlu subtype 5 (mGlu₅) have the potential to alleviate symptoms of numerous central nervous system disorders such as schizophrenia in a more targeted fashion. Multiple mGlu₅ positive allosteric modulators (PAMs), such as 1-(3-fluorophenyl)-N-((3-fluorophenyl)-methylideneamino)-methanimine (DFB), 3-cyano-N-(1,3-diphenyl-1H-pyrazol-5-yl)-benzamide (CDPPB), and 4-nitro-N-(1,3-diphenyl-1H-pyrazol-5-yl)-benzamide (VU-29), exert their actions by binding to a defined allosteric site on mGlu₅ located in the seven-transmembrane domain (7TM) and shared by mGlu₅ negative allosteric modulator (NAM) 2-methyl-6-(phenylethynyl)-pyridine (MPEP). Actions of the PAM N-{4-chloro-2-[(1,3-dioxo-1,3-dihydro-2H-isoindol-2-yl)methyl]phenyl}-2-hydroxybenzamide (CPPHA) are mediated by a distinct allosteric site in the 7TM domain different from the MPEP binding site. Experimental evidence confirms these findings through mutagenesis experiments involving residues F585 (TM1) and A809 (TM7). In an effort to investigate mGlu₅ PAM selectivity for this alternative allosteric site distinct from MPEP binding, we employed in silico quantitative structure-activity relationship (QSAR) modeling. Subsequent ligand-based virtual screening prioritized a set of 63 candidate compounds predicted from a library of over 4 million commercially available compounds to bind exclusively to this novel site. Experimental validation verified the biological activity for seven of 63 selected candidates. Further, medicinal chemistry optimizations based on these molecules revealed compound VU6003586 with an experimentally validated potency of 174 nM. Radioligand binding experiments showed only partial inhibition at very high concentrations, most likely indicative of binding at a non-MPEP site. Selective positive allosteric modulators for mGlu₅ have the potential for tremendous impact concerning devastating neurological disorders such as schizophrenia and Huntington's disease. These identified and validated novel selective compounds can serve as starting points for more specifically tailored lead and probe molecules and thus help the development of potential therapeutic agents with reduced adverse effects.

Autonomous Molecular Design: Then and Now

The success of deep machine learning in processing of large amounts of data, for example, in image or voice recognition and generation, raises the possibilities that these tools can also be applied for solving complex problems in materials science. In this forum article, we focus on molecular design that aims to answer the question on how we can predict and synthesize molecules with tailored physical, chemical, or biological properties. A potential answer to this question could be found by using intelligent systems that integrate physical models and computational machine learning techniques with automated synthesis and characterization tools. Such systems learn through every single experiment in an analogy to a human scientific expert. While the general idea of an autonomous system for molecular synthesis and characterization has been around for a while, its implementations for the materials sciences are sparse. Here we provide an overview of the developments in chemistry automation and the applications of machine learning techniques in the chemical and pharmaceutical industries with a focus on the novel capabilities that deep learning brings in.

Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design

In medicinal chemistry programs it is key to design and make compounds that are efficacious and safe. This is a long, complex, and difficult multiparameter optimization process, often including several properties with orthogonal trends. New methods for the automated design of compounds against profiles of multiple properties are thus of great value. Here we present a fragment-based reinforcement learning approach based on an actor-critic model, for the generation of novel molecules with optimal properties. The actor and the critic are both modeled with bidirectional long short-term memory (LSTM) networks. The AI method learns how to generate new compounds with desired properties by starting from an initial set of lead molecules and then improving these by replacing some of their fragments. A balanced binary tree based on the similarity of fragments is used in the generative process to bias the output toward structurally similar molecules. The method is demonstrated by a case study showing that 93% of the generated molecules are chemically valid and more than a third satisfy the targeted objectives, while there were none in the initial set.

A HTRF based competitive binding assay for screening specific inhibitors of HIV-1 capsid assembly targeting the C-Terminal domain of capsid

Due to its multifaceted essential roles in virus replication and extreme genetic fragility, the human immunodeficiency virus type 1 (HIV-1) capsid (CA) protein is a valued therapeutic target. However, CA is as yet unexploited clinically, as there are no antiviral agents that target it currently on the market. To facilitate the identification of potential HIV-1 CA inhibitors, we established a homogeneous time-resolved fluorescence (HTRF) assay to screen for small molecules that target a biologically active and specific binding pocket in the C-terminal domain of HIV-1 CA (CA CTD). The assay, which is based on competition of small molecules for the binding of a known CA inhibitor (CAI) to the CA CTD, exhibited a signal-to-background ratio (S/B) > 10 and a Z' value > 0.9. In a pilot screen of three kinase inhibitor libraries containing 464 compounds, we identified one compound, TX-1918, as a low micromolecular inhibitor of the HIV-1 CA CTD-CAI interaction (IC₅₀ = 3.81 μM) that also inhibited viral replication at moderate micromolar concentration (EC₅₀ = 15.16 μM) and inhibited CA assembly in vitro. Based on the structure of TX-1918, an additional compound with an antiviral EC₅₀ of 6.57 μM and cellular cytotoxicity CC₅₀ of 102.55 μM was obtained from a compound similarity search. Thus, the HTRF-based assay has properties that are suitable for screening large compound libraries to identify novel anti-HIV-1 inhibitors targeting the CA CTD.

A primer on natural product-based virtual screening

Databases play an important role in various computational techniques, including virtual screening (VS) and molecular modeling in general. These collections of molecules can contain a large amount of information, making them suitable for several drug discovery applications. For example, vendor, bioactivity data or target type can be found when searching a database. The introduction of these data resources and their characteristics is used for the design of an experiment. The description of the construction of a database can also be a good advisor for the creation of a new one. There are free available databases and commercial virtual libraries of molecules. Furthermore, a computational chemist can find databases for a general purpose or a specific subset such as natural products (NPs). In this chapter, NP database resources are presented, along with some guidelines when preparing an NP database for drug discovery purposes.

Rescoring Virtual Screening Results with the MM-PBSA Methods: Beware of Internal Dielectric Constants

With the potential of improving virtual screening outcome, MM-PB/GBSA has become a disputed method that requires extensive testing and tuning to provide the optimal results. One of the tuning factors is the internal or solute dielectric constant. We have applied three test sets with receptors of different categories and libraries from different sources to investigate the underlying issue related to this constant. We discovered that increasing internal dielectric value does not improve the virtual screening enrichment qualitatively. More interestingly, nonpolar and polar calculated energies act differently in libraries with different molecular weight distributions. From this work, the performance of MM-PBSA rescoring in virtual screening is more library- than receptor-dependent.

Computational advances in combating colloidal aggregation in drug discovery

Small molecule effectors are essential for drug discovery. Specific molecular recognition, reversible binding and dose-dependency are usually key requirements to ensure utility of a novel chemical entity. However, artefactual frequent-hitter and assay interference compounds may divert lead optimization and screening programmes towards attrition-prone chemical matter. Colloidal aggregates are the prime source of false positive readouts, either through protein sequestration or protein-scaffold mimicry. Nevertheless, assessment of colloidal aggregation remains somewhat overlooked and under-appreciated. In this Review, we discuss the impact of aggregation in drug discovery by analysing select examples from the literature and publicly-available datasets. We also examine and comment on technologies used to experimentally identify these potentially problematic entities. We focus on evidence-based computational filters and machine learning algorithms that may be swiftly deployed to flag chemical matter and mitigate the impact of aggregates in discovery programmes. We highlight the tools that can be used to scrutinize libraries, and identify and eliminate these problematic compounds.

Inhibition of Oncogenic Kinases: An In Vitro Validated Computational Approach Identified Potential Multi-Target Anticancer Compounds

Tumorigenesis in humans is a multistep progression that imitates genetic changes leading to cell transformation and malignancy. Oncogenic kinases play a central role in cancer progression, rendering them putative targets for the design of anti-cancer drugs. The presented work aims to identify the potential multi-target inhibitors of oncogenic receptor tyrosine kinases (RTKs) and serine/threonine kinases (STKs). For this, chemoinformatics and structure-based virtual screening approaches were combined with an in vitro validation of lead hits on both cancerous and non-cancerous cell lines. A total of 16 different kinase structures were screened against ~739,000 prefiltered compounds using diversity selection, after which the top hits were filtered for promising pharmacokinetic properties. This led to the identification of 12 and 9 compounds against RTKs and STKs, respectively. Molecular dynamics (MD) simulations were carried out to better comprehend the stability of the predicted hit kinase-compound complexes. Two top-ranked compounds against each kinase class were tested in vitro for cytotoxicity, with compound F34 showing the most promising inhibitory activity in HeLa, HepG2, and Vero cell lines with IC50 values of 145.46 μM, 175.48 μM, and 130.52 μM, respectively. Additional docking of F34 against various RTKs was carried out to support potential multi-target inhibition. Together with reliable MD simulations, these results suggest the promising potential of identified multi-target STK and RTK scaffolds for further kinase-specific anti-cancer drug development toward combinatorial therapies.

Structural optimization on a virtual screening hit of smoothened receptor

The Hedgehog (Hh) pathway plays a critical role during embryonic development by controlling cell patterning, growth and migration. In adults, the function of Hh pathway is curtailed to tissue repair and maintenance. Aberrant reactivation of Hh signaling has been linked to tumorigenesis in various cancers, such as basal cell carcinoma (BCC) and medulloblastoma. The Smoothened (Smo) receptor, a key component of the Hh pathway which is central to the signaling transduction, has emerged as an attractive therapeutic target for the treatment of human cancers. Taking advantage of the availability of several crystal structures of Smo in complex with different antagonists, we have previously conducted a molecular docking-based virtual screening to identify several compounds which exhibited significant inhibitory activity against the Hh pathway activation (IC₅₀ < 10 μM) in a Gli-responsive element (GRE) reporter gene assay. The most potent compound (ChemDiv ID C794-1677: 47 nM) showed comparable Hh signaling inhibition to the marketed drug vismodegib (46 nM). Herein, we report our structural optimization based on the virtual screening hit C794-1677. Our efforts are aimed to improve potency, decrease cLogP, and remove potentially metabolic labile/toxic pyrrole and aniline functionalities presented in C794-1677. The optimization led to the identification of numerous potent compounds exemplified by 25 (7.1 nM), which was 7 folds more potent compared with vismodegib. In addition, 25 was much less lipophilic compared with C794-1677 and devoid of the potentially metabolic labile/toxic pyrrole and aniline functional groups. Furthermore, 25 exhibited promising efficacy in inhibiting Gli1 mRNA expression in NIH3T3 cells with either wildtype Smo or D473H Smo mutant. These results represented significant improvement over the virtual screening hit C794-1677 and suggested that compound 25 can be used as a good starting point to support lead optimization.

Applications of Ensemble Docking in Potential Inhibitor Screening for Mycobacterium tuberculosis Isocitrate Lyase Using a Local Plant Database

Isocitrate lyase (ICL) is a persistent factor for the survival of dormant stage Mycobacterium tuberculosis (MTB), thus a potential drug target for tuberculosis treatment. In this work, ensemble docking approach was used to screen for potential inhibitors of ICL. The ensemble conformations of ICL active site were obtained from molecular dynamics simulation on three dimer form systems, namely the apo ICL, ICL in complex with metabolites (glyoxylate and succinate), and ICL in complex with substrate (isocitrate). Together with the ensemble conformations and the X-ray crystal structures, 22 structures were used for the screening against Malaysian Natural Compound Database (NADI). The top 10 compounds for each ensemble conformation were selected. The number of compounds was then further narrowed down to 22 compounds that were within the Lipinski's Rule of Five for drug-likeliness and were also docked into more than one ensemble conformation. Theses 22 compounds were furthered evaluate using whole cell assay. Some compounds were not commercially available; therefore, plant crude extracts were used for the whole cell assay. Compared to itaconate (the known inhibitor of ICL), crude extracts from Manilkara zapota, Morinda citrifolia, Vitex negundo, and Momordica charantia showed some inhibition activity. The MIC/MBC value were 12.5/25, 12.5/25, 0.78/1.6, and 0.39/1.6 mg/mL, respectively. This work could serve as a preliminary study in order to narrow the scope for high throughput screening in the future.

De Novo Molecular Design by Combining Deep Autoencoder Recurrent Neural Networks with Generative Topographic Mapping

Here we show that Generative Topographic Mapping (GTM) can be used to explore the latent space of the SMILES-based autoencoders and generate focused molecular libraries of interest. We have built a sequence-to-sequence neural network with Bidirectional Long Short-Term Memory layers and trained it on the SMILES strings from ChEMBL23. Very high reconstruction rates of the test set molecules were achieved (>98%), which are comparable to the ones reported in related publications. Using GTM, we have visualized the autoencoder latent space on the two-dimensional topographic map. Targeted map zones can be used for generating novel molecular structures by sampling associated latent space points and decoding them to SMILES. The sampling method based on a genetic algorithm was introduced to optimize compound properties "on the fly". The generated focused molecular libraries were shown to contain original and a priori feasible compounds which, pending actual synthesis and testing, showed encouraging behavior in independent structure-based affinity estimation procedures (pharmacophore matching, docking).

Design, Synthesis and Biological Evaluation of Spiro Cyclohexane-1,2- Quinazoline Derivatives as Potent Dipeptidyl Peptidase IV Inhibitors

OBJECTIVE

Inhibition of dipeptidyl peptidase IV (DPP-4) is currently one of the most valuable and potential chemotherapeutic regimes for the medication of Type 2 Diabetes Mellitus (T2DM).

METHOD

Based on linagliptin, this study discusses the design, synthesis and biological evaluation of spiro cyclohexane-1,2'-quinazoline scaffold hybridized with various heterocyclic ring systems through different atomic spacers as a highly potent DPP-4 inhibitors. DPP-4 enzyme assay represented that most of the target compounds are 102-103 folds more active than the reference drug linagliptin (IC50: 0.0005-0.0089 nM vs 0.77 nM; respectively). Moreover, in vivo oral hypoglycemic activity assay revealed that most of the tested candidates were more potent than the reference drug, sitagliptin, producing rapid onset with long duration of activity that extends to 24 h. Interestingly, the derivatives 11, 16, 18a and 23 showed evidence of mild cytochrome P450 3A4 (CYP3A4) inhibition (IC50; > 210 µM) and their acute toxicity (LD50) was more than 1.9 gm/kg. Molecular simulation study of the new quinazoline derivatives explained the obtained biological results.

CONCLUSION

Finally, we conclude that our target compounds could be highly beneficial for diabetic patients in the clinic.

An Overview, Advantages and Therapeutic Potential of Nonpeptide Positive Allosteric Modulators of Glucagon-Like Peptide-1 Receptor

Due to uncomfortable injection regimens of peptidic agonists of glucagon-like peptide-1 receptor (GLP-1R), orally available nonpeptide positive allosteric modulators (PAMs) of GLP-1Rs are foreseen as the possible future mainstream therapy for type 2 diabetes. Herein, current GLP-1R PAMs are reviewed. Based on the effectiveness and in silico predicted physicochemical properties, pharmacokinetics, and toxicity, possible candidates for further development as oral drugs were selected. The suggestion is that GLP-1R PAMs might be used orally alone or in combination with dipeptidyl peptidase-4 (DPP-4) inhibitors, which could offer an optimal treatment option next to metformin monotherapy in type 2 diabetes mellitus, or in a wider spectrum of indications. Quercetin acts as a GLP-1R PAM and DPP-4 inhibitor, and therefore, might be considered as a pioneering agent with a dual mechanism of action, in terms of GLP-1R positive allosteric modulation and DPP-4 inhibition for potentiating GLP-1 dependent effects.

SAnDReS: A Computational Tool for Docking

Since the early 1980s, we have witnessed considerable progress in the development and application of docking programs to assess protein-ligand interactions. Most of these applications had as a goal the identification of potential new binders to protein targets. Another remarkable progress is taking place in the determination of the structures of protein-ligand complexes, mostly using X-ray diffraction crystallography. Considering these developments, we have a favorable scenario for the creation of a computational tool that integrates into one workflow all steps involved in molecular docking simulations. We had these goals in mind when we developed the program SAnDReS. This program allows the integration of all computational features related to modern docking studies into one workflow. SAnDReS not only carries out docking simulations but also evaluates several docking protocols allowing the selection of the best approach for a given protein system. SAnDReS is a free and open-source (GNU General Public License) computational environment for running docking simulations. Here, we describe the combination of SAnDReS and AutoDock4 for protein-ligand docking simulations. AutoDock4 is a free program that has been applied to over a thousand receptor-ligand docking simulations. The dataset described in this chapter is available for downloading at https://github.com/azevedolab/sandres.

How to Prepare a Compound Collection Prior to Virtual Screening

Virtual screening is a well-established technique that has proven to be successful in the identification of novel biologically active molecules, including drug repurposing. Whether for ligand-based or for structure-based virtual screening, a chemical collection needs to be properly processed prior to in silico evaluation. Here we describe our step-by-step procedure for handling very large collections (up to billions) of compounds prior to virtual screening.

Screening Library Design

Thanks to technological advances and a greater understanding of the biological and chemical natures of targets and related diseases, high-throughput screening (HTS) has been allowed to be faster, cheaper, and more accessible. Yet, despite these increased technologies and understandings, the frequency of novel and drugs are being approved each year has not being increasing over the years. 2017 was considered a "bumper" year with a total of 46 approved drugs, over double that of the previous year. However, it is thought that part of the problem that HTS has not lived up to expectations is because of the contents of current chemical libraries. Therefore, new methods to design screening libraries are of great interest.

In silico identification of inhibitors targeting N-Terminal domain of human Replication Protein A

Replication Protein A (RPA) mediates DNA Damage Response (DDR) pathways through protein-protein interactions (PPIs). Targeting the PPIs formed between RPA and other DNA Damage Response (DDR) mediators has become an intriguing area of research for cancer drug discovery. A number of studies applied different methods ranging from high throughput screening approaches to fragment-based drug design tools to discover RPA inhibitors. Although these methods are robust, virtual screening approaches may be allocated as an alternative to such experimental methods, especially for screening of large libraries. Here we report the comprehensive screening of the large database, ZINC15 composed of ∼750 M compounds and the comparison of the identified ligands with the previously known inhibitors by means of binding affinity and drug-likeness. Initially, a ligand library sharing similarity with a promising inhibitor of the N-terminal domain of the RPA70 subunit (RPA70N) was generated by screening of the ZINC15 library. 46,999 ligands were collected and screened by LeDock which produced a satisfactory correlation with the experimental values (R² = 0.77). 10 of the top-scoring ligands in LeDock were directly progressed to molecular dynamics (MD) simulations, while 10 additional ligands were also selected based on their LeDock scores and the presence of a functional group that could interact with the key amino acids in the RPA70N cleft. MD simulations were used to predict the binding free energy of the ligands by the MM-PBSA method which produced a high level of agreement with the experiments (R² = 0.85). Binding free energy predictions pointed out 2 ligands with higher binding affinity than any of the reference inhibitors. Particularly the ligand ZINC000753854163 exhibited superior drug-likeness features than any of the known inhibitors. Overall, this study reports ZINC000753854163 as a possible inhibitor of RPA70N, reflecting its possible use in RPA70N targeted cancer therapy.

Investigating the Behavior of Published PAINS Alerts Using a Pharmaceutical Company Data Set

Biochemical assay interference is becoming increasingly recognized as a significant waste of resource in drug discovery, both in industry and academia. A seminal publication from Baell and Holloway raised the awareness of this issue, and they published a set of alerts to identify what they described as PAINS (pan-assay interference compounds). These alerts have been taken up by drug discovery groups, even though the original paper had a somewhat limited data set. Here, we have taken Lilly’s far larger internal data set to assess the PAINS alerts on four criteria: promiscuity (over six assay formats including AlphaScreen), compound stability, cytotoxicity, and presence of a high Hill slope as a surrogate for non-1:1 protein–ligand binding. It was found that only three of the alerts show pan-assay promiscuity, and the alerts appear to encode primarily AlphaScreen promiscuous molecules. Although not enriching for pan-assay promiscuity, many of the alerts do encode molecules that are unstable, show cytotoxicity, and increase the prevalence of high Hill slopes.

QSAR Modeling of ToxCast Assays Relevant to the Molecular Initiating Events of AOPs Leading to Hepatic Steatosis

Nonalcoholic hepatic steatosis is a worldwide epidemiological concern since it is among the most prominent hepatic diseases. Indeed, research in toxicology and epidemiology has gathered evidence that exposure to endocrine disruptors can perturb cellular homeostasis and cause this disease. Therefore, assessing the likelihood of a chemical to trigger hepatic steatosis is a matter of the utmost importance. However, systematic in vivo testing of all the chemicals humans are exposed to is not feasible for ethical and economical reasons. In this context, predicting the molecular initiating events (MIE) leading to hepatic steatosis by QSAR modeling is an issue of practical relevance in modern toxicology. In this article, we present QSAR models based on random forest classifiers and DRAGON molecular descriptors for the prediction of in vitro assays that are relevant to MIEs leading to hepatic steatosis. These assays were provided by the ToxCast program and proved to be predictive for the detection of chemical-induced steatosis. During the modeling process, special attention was paid to chemical and toxicological data curation. We adopted two modeling strategies (undersampling and balanced random forests) to develop robust QSAR models from unbalanced data sets. The two modeling approaches gave similar results in terms of predictivity, and most of the models satisfy a minimum percentage of correctly predicted chemicals equal to 75%. Finally, and most importantly, the developed models proved to be useful as an effective in silico screening test for hepatic steatosis.

In silico design of novel proton-pump inhibitors with reduced adverse effects

The development of new proton-pump inhibitors (PPIs) with less adverse effects by lowering the pKa values of nitrogen atoms in pyrimidine rings has been previously suggested by our group. In this work, we proposed that new PPIs should have the following features: (1) number of ring II = number of ring I + 1; (2) preferably five, six, or seven-membered heteroatomic ring for stability; and (3) 1 < pKa1 < 4. Six molecular scaffolds based on the aforementioned criteria were constructed, and R groups were extracted from compounds in extensive data sources. A virtual molecule dataset was established, and the pKa values of specific atoms on the molecules in the dataset were calculated to select the molecules with required pKa values. Drug-likeness screening was further conducted to obtain the candidates that significantly reduced the adverse effects of long-term PPI use. This study provided insights and tools for designing targeted molecules in silico that are suitable for practical applications.

Dynamic structure based pharmacophore modeling of the Acetylcholinesterase reveals several potential inhibitors

Acetylcholinesterase is a critical enzyme that regulates neurotransmission by catalyzing the breakdown of neurotransmitter acetylcholine in synapses of the nervous system. It is an important target for therapeutic drugs that treat Alzheimer's disease. Since, the degree of flexibility of the side chains of the residues in the active-site gorge of Acetylcholinesterase is diverse it results in different bound ligand conformations. The side-chain conformations of Ser293, Tyr341, Leu76, and Val73 are flexible, while the side-chain conformations of Tyr72, Tyr 124, Ser125, Phe295, and Arg296 appear to be fixed. In this study, multi-conformation dynamic pharmacophore models from the donepezyl-binding pocket based on highly populated structures chosen from molecular dynamics simulations were used for screening compounds that can properly bind acetylcholinesterase. Based on these structures, three pharmacophore models were generated. Consequently, 14 hits were retrieved as final candidates by utilizing virtual screening of ZINC database and molecular docking.