Contemporary Computational Applications and Tools in Drug Discovery

Practical Three-Component Regioselective Synthesis of Drug-Like 3-Aryl(or heteroaryl)-5,6-dihydrobenzo[h]cinnolines as Potential Non-Covalent Multi-Targeting Inhibitors To Combat Neurodegenerative Diseases

Neurodegenerative diseases (NDs) are one of the prominent health challenges facing contemporary society, and many efforts have been made to overcome and (or) control it. In this research paper, we described a practical one-pot two-step three-component reaction between 3,4-dihydronaphthalen-1(2H)-one (1), aryl(or heteroaryl)glyoxal monohydrates (2a-h), and hydrazine monohydrate (NH2NH2•H2O) for the regioselective preparation of some 3-aryl(or heteroaryl)-5,6-dihydrobenzo[h]cinnoline derivatives (3a-h). After synthesis and characterization of the mentioned cinnolines (3a-h), the in silico multi-targeting inhibitory properties of these heterocyclic scaffolds have been investigated upon various Homo sapiens-type enzymes, including hMAO-A, hMAO-B, hAChE, hBChE, hBACE-1, hBACE-2, hNQO-1, hNQO-2, hnNOS, hiNOS, hPARP-1, hPARP-2, hLRRK-2(G2019S), hGSK-3β, hp38α MAPK, hJNK-3, hOGA, hNMDA receptor, hnSMase-2, hIDO-1, hCOMT, hLIMK-1, hLIMK-2, hRIPK-1, hUCH-L1, hPARK-7, and hDHODH, which have confirmed their functions and roles in the neurodegenerative diseases (NDs), based on molecular docking studies, and the obtained results were compared with a wide range of approved drugs and well-known (with IC50, EC50, etc.) compounds. In addition, in silico ADMET prediction analysis was performed to examine the prospective drug properties of the synthesized heterocyclic compounds (3a-h). The obtained results from the molecular docking studies and ADMET-related data demonstrated that these series of 3-aryl(or heteroaryl)-5,6-dihydrobenzo[h]cinnolines (3a-h), especially hit ones, can really be turned into the potent core of new drugs for the treatment of neurodegenerative diseases (NDs), and/or due to the having some reactionable locations, they are able to have further organic reactions (such as cross-coupling reactions), and expansion of these compounds (for example, with using other types of aryl(or heteroaryl)glyoxal monohydrates) makes a new avenue for designing novel and efficient drugs for this purpose.

Augmenting DMTA using predictive AI modelling at AstraZeneca

Design-Make-Test-Analyse (DMTA) is the discovery cycle through which molecules are designed, synthesised, and assayed to produce data that in turn are analysed to inform the next iteration. The process is repeated until viable drug candidates are identified, often requiring many cycles before reaching a sweet spot. The advent of artificial intelligence (AI) and cloud computing presents an opportunity to innovate drug discovery to reduce the number of cycles needed to yield a candidate. Here, we present the Predictive Insight Platform (PIP), a cloud-native modelling platform developed at AstraZeneca. The impact of PIP in each step of DMTA, as well as its architecture, integration, and usage, are discussed and used to provide insights into the future of drug discovery.

Shaping the Future of Obesity Treatment: In Silico Multi-Modeling of IP6K1 Inhibitors for Obesity and Metabolic Dysfunction

Recent research has uncovered a promising approach to addressing the growing global health concern of obesity and related disorders. The inhibition of inositol hexakisphosphate kinase 1 (IP6K1) has emerged as a potential therapeutic strategy. This study employs multiple ligand-based in silico modeling techniques to investigate the structural requirements for benzisoxazole derivatives as IP6K1 inhibitors. Firstly, we developed linear 2D Quantitative Structure-Activity Relationship (2D-QSAR) models to ensure both their mechanistic interpretability and predictive accuracy. Then, ligand-based pharmacophore modeling was performed to identify the essential features responsible for the compounds' high activity. To gain insights into the 3D requirements for enhanced potency against the IP6K1 enzyme, we employed multiple alignment techniques to set up 3D-QSAR models. Given the absence of an available X-ray crystal structure for IP6K1, a reliable homology model for the enzyme was developed and structurally validated in order to perform structure-based analyses on the selected dataset compounds. Finally, molecular dynamic simulations, using the docked poses of these compounds, provided further insights. Our findings consistently supported the mechanistic interpretations derived from both ligand-based and structure-based analyses. This study offers valuable guidance on the design of novel IP6K1 inhibitors. Importantly, our work exclusively relies on non-commercial software packages, ensuring accessibility for reproducing the reported models.

Total Synthesis as Training for Medicinal Chemistry

There is an ongoing debate about the best types of training in academia for practicing modern medicinal chemistry in the pharmaceutical and biotechnology industries of today. A case is made in this Viewpoint for the ongoing, and perhaps increasing, value of total synthesis as training for medicinal chemistry.

Exploring the Role of Chemoinformatics in Accelerating Drug Discovery: A Computational Approach

Cheminformatics and its role in drug discovery is expected to be the privileged approach in handling large number of chemical datasets. This approach contributes toward the pharmaceutical development and assessment of chemical compounds at a faster rate efficiently. Additionally, as technological advancement impacts research, cheminformatics is being used more and more in the field of health science. This chapter describes the concepts of cheminformatics along with its involvement in drug discovery with a case study.

Computer-Aided Drug Discovery and Design: Recent Advances and Future Prospects

Computer-aided drug discovery and design involve the use of information technologies to identify and develop, on a rational ground, chemical compounds that align a set of desired physicochemical and biological properties. In its most common form, it involves the identification and/or modification of an active scaffold (or the combination of known active scaffolds), although de novo drug design from scratch is also possible. Traditionally, the drug discovery and design processes have focused on the molecular determinants of the interactions between drug candidates and their known or intended pharmacological target(s). Nevertheless, in modern times, drug discovery and design are conceived as a particularly complex multiparameter optimization task, due to the complicated, often conflicting, property requirements.This chapter provides an updated overview of in silico approaches for identifying active scaffolds and guiding the subsequent optimization process. Recent groundbreaking advances in the field have also analyzed the integration of state-of-the-art machine learning approaches in every step of the drug discovery process (from prediction of target structure to customized molecular docking scoring functions), integration of multilevel omics data, and the use of a diversity of computational approaches to assist target validation and assess plausible binding pockets.

NMR and Docking Calculations Reveal Novel Atomistic Selectivity of a Synthetic High-Affinity Free Fatty Acid vs. Free Fatty Acids in Sudlow's Drug Binding Sites in Human Serum Albumin

Saturation transfer difference (STD), inter-ligand NOEs (INPHARMA NMR), and docking calculations are reported for investigating specific binding sites of the high-affinity synthetic 7-nitrobenz-2-oxa-1,3-diazoyl-4-C12 fatty acid (NBD-C12 FA) with non-labeled human serum albumin (HSA) and in competition with the drugs warfarin and ibuprofen. A limited number of negative interligand NOEs between NBD-C12 FA and warfarin were interpreted in terms of a short-range allosteric competitive binding in the wide Sudlow's binding site II (FA7) of NBD-C12 FA with Ser-202, Lys-199, and Trp-214 and warfarin with Arg-218 and Arg-222. In contrast, the significant number of interligand NOEs between NBD-C12 FA and ibuprofen were interpreted in terms of a competitive binding mode in Sudlow's binding site I (FA3 and FA4) with Ser-342, Arg-348, Arg-485, Arg-410, and Tyr-411. NBD-C12 FA has the unique structural properties, compared to short-, medium-, and long-chain saturated and unsaturated natural free fatty acids, of interacting with well-defined structures with amino acids of both the internal and external polar anchor sites in Sudlow's binding site I and with amino acids in both FA3 and FA4 in Sudlow's binding site II. The NBD-C12 FA, therefore, interacts with novel structural characteristics in the drug binding sites I and II and can be regarded as a prototype molecule for drug development.

Accelerating Alchemical Free Energy Prediction Using a Multistate Method: Application to Multiple Kinases

Alchemical free-energy methods based on molecular dynamics (MD) simulations have become important tools to identify modifications of small organic molecules that improve their protein binding affinity during lead optimization. The routine application of pairwise free-energy methods to rank potential binders from best to worst is impacted by the combinatorial increase in calculations to perform when the number of molecules to assess grows. To address this fundamental limitation, our group has developed replica-exchange enveloping distribution sampling (RE-EDS), a pathway-independent multistate method, enabling the calculation of alchemical free-energy differences between multiple ligands (N > 2) from a single MD simulation. In this work, we apply the method to a set of four kinases with diverse binding pockets and their corresponding inhibitors (42 in total), chosen to showcase the general applicability of RE-EDS in prospective drug design campaigns. We show that for the targets studied, RE-EDS is able to model up to 13 ligands simultaneously with high sampling efficiency, leading to a substantial decrease in computational cost when compared to pairwise methods.

DAIKON: A Data Acquisition, Integration, and Knowledge Capture Web Application for Target-Based Drug Discovery

Primitive data organization practices struggle to deliver at the scale and consistency required to meet multidisciplinary collaborations in drug discovery. For effective data sharing and coordination, a unified platform that can collect and analyze scientific information is essential. We present DAIKON, an open-source framework that integrates targets, screens, hits, and manages projects within a target-based drug discovery portfolio. Its knowledge capture components enable teams to record subsequent molecules as their properties improve, facilitate team collaboration through discussion threads, and include modules that visually illustrate the progress of each target as it advances through the pipeline. It serves as a repository for scientists sourcing data from Mycobrowser, UniProt, PDB. The goal is to globalize several variations of the drug-discovery program without compromising local aspects of specific workflows. DAIKON is modularized by abstracting the database and creating separate layers for entities, business logic, infrastructure, APIs, and frontend, with each tier allowing for extensions. Using Docker, the framework is packaged into two solutions: daikon-server-core and daikon-client. Organizations may deploy the project to on-premises servers or VPC. Active-Directory/SSO is supported for user administration. End users can access the application with a web browser. Currently, DAIKON is implemented in the TB Drug Accelerator program (TBDA).

Synthesis and Investigation of Anti-Inflammatory Activity of New Thiourea Derivatives of Naproxen

The aim of the study was a synthesis and investigation of the dose-dependent anti-inflammatory effect of new thiourea derivatives of naproxen with selected aromatic amines and esters of aromatic amino acids. The results of the in vivo study indicate that derivatives of m-anisidine (4) and N-methyl tryptophan methyl ester (7) showed the most potent anti-inflammatory activity four hours after injection of carrageenan, with the percentage of inhibition of 54.01% and 54.12%, respectively. In vitro assays of COX-2 inhibition demonstrated that none of the tested compounds achieved 50% inhibition at concentrations lower than 100 µM. On the other hand, the aromatic amine derivatives (1-5) accomplished significant inhibition of 5-LOX, and the lowest IC₅₀ value was observed for compound 4 (0.30 μM). High anti-edematous activity of compound 4 in the rat paw edema model, together with potent inhibition of 5-LOX, highlight this compound as a promising anti-inflammatory agent.

Green and efficient one-pot three-component synthesis of novel drug-like furo[2,3–d]pyrimidines as potential active site inhibitors and putative allosteric hotspots modulators of both SARS-CoV-2 MPro and PLPro

In this paper, an environmentally benign, convenient, and efficient one-pot three-component reaction has been developed for the regioselective synthesis of novel 5-aroyl(or heteroaroyl)-6-(alkylamino)-1,3-dimethylfuro[2,3-d]pyrimidine-2,4(1H,3H)-diones (4a‒n) through the sequential condensation of aryl(or heteroaryl)glyoxal monohydrates (1a‒g), 1,3-dimethylbarbituric acid (2), and alkyl(viz. cyclohexyl or tert-butyl)isocyanides (3a or 3b) catalyzed by ultra-low loading ZrOCl₂•8H₂O (just 2 mol%) in water at 50 ˚C. After synthesis and characterization of the mentioned furo[2,3-d]pyrimidines (4a‒n), their multi-targeting inhibitory properties were investigated against the active site and putative allosteric hotspots of both SARS-CoV-2 main protease (M^Pro) and papain-like protease (PL^Pro) based on molecular docking studies and compare the attained results with various medicinal compounds which approximately in three past years were used, introduced, and or repurposed to fight against COVID-19. Furthermore, drug-likeness properties of the mentioned small heterocyclic frameworks (4a‒n) have been explored using in silico ADMET analyses. Interestingly, the molecular docking studies and ADMET-related data revealed that the novel series of furo[2,3-d]pyrimidines (4a‒n), especially 5-(3,4-methylendioxybenzoyl)-6-(cyclohexylamino)-1,3-dimethylfuro[2,3-d]pyrimidine-2,4(1H,3H)-dione (4g) as hit one is potential COVID-19 drug candidate, can subject to further in vitro and in vivo studies. It is worthwhile to note that the protein-ligand-type molecular docking studies on the human body temperature-dependent M^Pro protein that surprisingly contains zinc^II (Zn^II) ion between His41/Cys145 catalytic dyad in the active site, which undoubtedly can make new plans for designing novel SARS-CoV-2 M^Pro inhibitors, is performed for the first time in this paper, to the best of our knowledge.

Traditional Machine and Deep Learning for Predicting Toxicity Endpoints

Molecular structure property modeling is an increasingly important tool for predicting compounds with desired properties due to the expensive and resource-intensive nature and the problem of toxicity-related attrition in late phases during drug discovery and development. Lately, the interest for applying deep learning techniques has increased considerably. This investigation compares the traditional physico-chemical descriptor and machine learning-based approaches through autoencoder generated descriptors to two different descriptor-free, Simplified Molecular Input Line Entry System (SMILES) based, deep learning architectures of Bidirectional Encoder Representations from Transformers (BERT) type using the Mondrian aggregated conformal prediction method as overarching framework. The results show for the binary CATMoS non-toxic and very-toxic datasets that for the former, almost equally balanced, dataset all methods perform equally well while for the latter dataset, with an 11-fold difference between the two classes, the MolBERT model based on a large pre-trained network performs somewhat better compared to the rest with high efficiency for both classes (0.93-0.94) as well as high values for sensitivity, specificity and balanced accuracy (0.86-0.87). The descriptor-free, SMILES-based, deep learning BERT architectures seem capable of producing well-balanced predictive models with defined applicability domains. This work also demonstrates that the class imbalance problem is gracefully handled through the use of Mondrian conformal prediction without the use of over- and/or under-sampling, weighting of classes or cost-sensitive methods.

Advancing New Chemical Modalities into Clinical Studies

Drug discovery and development has experienced an incredible paradigm shift in the past two decades. What once was considered a predominant R&D landscape of small molecules within a prescribed properties and mechanism space now includes an innovative wave of new chemical modalities. Scientists in the pharmaceutical industry can now strategize across a variety of modalities to find the best option to modulate a given target and provide treatment for a specific disease. We have witnessed a remarkable change not only in molecular design but also in creative approaches to drug delivery that have enabled advancement of novel modalities to clinical studies. In this Microperspective, we evaluate the critical differences between traditional small molecules and beyond rule of 5 compounds, peptides, oligonucleotides, and biologics for advancing into development, particularly their pharmacokinetic profiles and drug delivery strategies.