Computer-aided Drug Discovery Research Articles

Quantitative Structure-Price Relationship (QS$R) Modeling and the Development of Economically Feasible Drug Discovery Projects.

In recent years, the field of quantitative structure-activity/property relationship (QSAR/QSPR) modeling has developed into a stable technology capable of reliably predicting new bioactive molecules. With the availability of inexpensive commercial sources of both synthetic chemicals and bioactivity assays, a cheminformatics-savvy scientist can readily establish a virtual drug discovery enterprise. A skilled computational chemist can not only develop a computer-aided drug discovery pipeline but also acquire or have the drug candidates made inexpensively for economical screening of desired on-target activity, critical off-target effects, and essential drug-likeness properties. As part of our drug discovery pipeline, a novel machine-learning model was built to relate chemical structures of synthetically accessible molecules to their prices. The model was trained from our "in stock" and "made on demand" diverse chemical entities, ranging in price from $20 to >$10,000. This novel model is encoded here as the quantitative structure-price relationship (QS$R) model.

BCL::MolAlign: Three-Dimensional Small Molecule Alignment for Pharmacophore Mapping.

Small molecule flexible alignment is a critical component of both ligand- and structure-based methods in computer-aided drug discovery. Despite its importance, the availability of high-quality flexible alignment software packages is limited. Here, we present BCL::MolAlign, a freely available property-based molecular alignment program. BCL::MolAlign accommodates ligand flexibility through a combination of pregenerated conformers and on-the-fly bond rotation. BCL::MolAlign converges on alignment poses by sampling the relative orientations of mutually matching atom pairs between molecules through Monte Carlo Metropolis sampling. Across six diverse ligand data sets, BCL::MolAlign flexible alignment outperforms MOE, ROCS, and FLEXS in recovering native ligand binding poses. Moreover, the BCL::MolAlign alignment score is more predictive of ligand activity than maximum common substructure similarity across 10 data sets. Finally, on a recently published benchmark set of 20 high quality congeneric ligand-protein complexes, BCL::MolAlign is able to recover a larger fraction of native binding poses than maximum common substructure-based alignment and RosettaLigand. BCL::MolAlign can be obtained as part of the Biology and Chemistry Library (BCL) software package freely with an academic license or can be accessed via Web server at http://meilerlab.org/index.php/servers/molalign .

Cancer Biology Aspects of Computational Methods & Applications in Drug Discovery.

Cancer is one of the most debilitating diseases worldwide; even though advances in molecular and cellular biology have contributed to the decline of mortality associated with cancer, the procedure of drug discovery and development of cancer are time-consuming and expensive. However, with computer-aided drug discovery (CADD) techniques, pharmaceutical firms can save production costs and reduce the time of introducing effective anticancer drugs for clinical trials. CADD strategies like structure-based drug designing, ligandbased drug designing, and combined structure-based and ligand-based approaches also have the advantage of identifying target sites and discovering active compounds with high affinity for the target sites. In this article, research carried out on cancer biology aspect of the computational approaches in drug discovery technology have been reviewed. The main objective of the study is to identify the potential causes and the development of the cancer. In addition to this, its recovery has been discussed briefly. Our findings indicate that only a few studies have been carried out regarding this area. Hence, it is recommended that further researches should be conducted on the computational methods for identifying candidate drugs for breast, pancreatic, colon, prostate, and other types of cancer.

Parametric Models for Understanding Atomic Trajectories in Different Domains of Lung Cancer Causing Protein

Non-small cell lung cancer (NSCLC) is a major cause of death worldwide. About 80% to 85% of lung cancer cases are NSCLC. It is well known that mutation of the epidermal growth factor (EGFR) may lead to the NSCLC. The first generation drugs are effective initially, but almost all patients develop drug resistance after about a year due to a secondary mutation. The computational methods are an efficient tool for investigating drug resistance, design, and discovery. Moreover, molecular dynamics (MD) simulation enables us to study and analyze the behavior of proteins and molecules at the atomic level. MD simulations offer extraordinary insight about biomolecules and are a valuable tool for computer aided drug discovery. Earlier studies on EGFR only focused on the kinase domain. Because EGFR is a multi-domain protein, mutations in the kinase domain may affect the function in other domains. Therefore, it is important to investigate the complete structure of the EGFR and its mutants. In this paper, we first generate the complete structure of the EGFR and perform MD simulation for the wildtype EGFR, EGFR with L858R mutation and EGFR with L858R and T790M mutation. We divide the complete structure of the EGFR and its mutants into 8 domains according to the reference crystal structure. We then consider atom trajectories as time series signals and estimate the power spectral densities using the auto-regressive integrated (ARI) model, which shows interesting insight. Dynamic time warping is used to analyze the similarity between each domain of the structures. Interesting patterns are observed which may be useful for investigating drug resistance and design. Furthermore, Pearson correlation coefficient, peaks, and widths of the power spectral density are calculated for each domain. The simulation results provide useful insight about conformation dynamics of the EGFR, such as atom motion and protein stability. The domains are less correlated in L858R type and even weaker when the second mutation occurs. The warping patterns are changed due to mutation and the movement of atoms is distorted. Hence, it is difficult for a drug to bind to the protein. These findings will be useful in understanding the characteristics of the EGFR and for computer aided drug design process for the NSCLC patients.

Molecular Docking Simulations with ArgusLab.

Molecular docking is the major computational technique employed in the early stages of computer-aided drug discovery. The availability of free software to carry out docking simulations of protein-ligand systems has allowed for an increasing number of studies using this technique. Among the available free docking programs, we discuss the use of ArgusLab ( http://www.arguslab.com/arguslab.com/ArgusLab.html ) for protein-ligand docking simulation. This easy-to-use computational tool makes use of a genetic algorithm as a search algorithm and a fast scoring function that allows users with minimal experience in the simulations of protein-ligand simulations to carry out docking simulations. In this chapter, we present a detailed tutorial to perform docking simulations using ArgusLab.

Therapeutic Inhibition of Myc in Cancer. Structural Bases and Computer-Aided Drug Discovery Approaches.

Myc (avian myelocytomatosis viral oncogene homolog) represents one of the most sought after drug targets in cancer. Myc transcription factor is an essential regulator of cell growth, but in most cancers it is overexpressed and associated with treatment-resistance and lethal outcomes. Over 40 years of research and drug development efforts did not yield a clinically useful Myc inhibitor. Drugging the “undruggable” is problematic, as Myc inactivation may negatively impact its physiological functions. Moreover, Myc is a disordered protein that lacks effective binding pockets on its surface. It is well established that the Myc function is dependent on dimerization with its obligate partner, Max (Myc associated factor X), which together form a functional DNA-binding domain to activate genomic targets. Herein, we provide an overview of the knowledge accumulated to date on Myc regulation and function, its critical role in cancer, and summarize various strategies that are employed to tackle Myc-driven malignant transformation. We focus on important structure-function relationships of Myc with its interactome, elaborating structural determinants of Myc-Max dimer formation and DNA recognition exploited for therapeutic inhibition. Chronological development of small-molecule Myc-Max prototype inhibitors and corresponding binding sites are comprehensively reviewed and particular emphasis is placed on modern computational drug design methods. On the outlook, technological advancements may soon provide the so long-awaited Myc-Max clinical candidate.

Development of an Information System of Structures and Force Field Parameters of Chemical Compounds from Sri Lankan Flora.

Sri Lanka offers a huge diversity of flora with a large proportion of those being endemic to the island. Both the endemic and native plants species serve as a rich bank of phytochemicals. In this study, "Sri Lankan Flora" an online web-based information system of phytochemical compounds isolated from the flora of Sri Lanka was proposed. The database contained 3D structures of those compounds, calculated quantitativestructure- activity relationship (QSAR) data and the GROMOS 54a7 force field parameters for each and every compound. The manually curated chemical structures, activities and force field parameters provide a possible direct avenue for computer-aided drug discovery. The present study is a continuing project with a wider goal of building up a database, not only for assisting the computeraided drug designing process, but also for other chemical applications, as the database includes structural, physical, chemical and dynamic properties of chemical compounds of the flora of Sri Lanka. The database is freely accessible at http://science.cmb.ac.lk/tools/slflora.

Computer-Aided Discovery of Small Molecule Inhibitors of Transcriptional Activity of TLX (NR2E1) Nuclear Receptor.

Orphan nuclear receptor TLX (NR2E1) plays a critical role in the regulation of neural stem cells (NSC) as well as in the development of NSC-derived brain tumors. In the last years, new data have emerged implicating TLX in prostate and breast cancer. Therefore, inhibitors of TLX transcriptional activity may have a significant impact on the treatment of several critical malignancies. However, the TLX protein possesses a non-canonical ligand-binding domain (LBD), which lacks a ligand-binding pocket (conventionally targeted in case of nuclear receptors) that complicates the development of small molecule inhibitors of TLX. Herein, we utilized a rational structure-based design approach to identify small molecules targeting the Atro-box binding site of human TLX LBD. As a result of virtual screening of ~7 million molecular structures, 97 compounds were identified and evaluated in the TLX-responsive luciferase reporter assay. Among those, three chemicals demonstrated 40–50% inhibition of luciferase-detected transcriptional activity of the TLX orphan nuclear receptor at a dose of 35 µM. The identified compounds represent the first class of small molecule inhibitors of TLX transcriptional activity identified via methods of computer-aided drug discovery.

Identification of new inhibitors of Mdm2-p53 interaction via pharmacophore and structure-based virtual screening.

BackgroundThe tumor suppressor protein p53 plays an important role in preventing tumor formation and progression through its involvement in cell division control and initiation of apoptosis. Mdm2 protein controls the activity of p53 protein through working as ubiquitin E3 ligase promoting p53 degradation through the proteasome degradation pathway. Inhibitors for Mdm2-p53 interaction have restored the activity of p53 protein and induced cancer fighting properties in the cell.PurposeThe objective of this study is to use computer-aided drug discovery techniques to search for new Mdm2-p53 interaction inhibitors.MethodsA set of pharmacophoric features were created based on a standard Mdm2 inhibitor and this was used to screen a commercial drug-like ligand library; then potential inhibitors were docked and ranked in a multi-step protocol using GLIDE. Top ranked ligands from docking were evaluated for their inhibition activity of Mdm2-p53 interaction using ELISA testing.ResultsSeveral compounds showed inhibition activity at the submicromolar level, which is comparable to the standard inhibitor Nutlin-3a. Furthermore, the discovered inhibitors were evaluated for their anticancer activities against different breast cancer cell lines, and they showed an interesting inhibition pattern.ConclusionThe reported inhibitors can represent a starting point for further SAR studies in the future and can help in the discovery of new anticancer agents.

Protein Family-Specific Models Using Deep Neural Networks and Transfer Learning Improve Virtual Screening and Highlight the Need for More Data.

Machine learning has shown enormous potential for computer-aided drug discovery. Here we show how modern convolutional neural networks (CNNs) can be applied to structure-based virtual screening. We have coupled our densely connected CNN (DenseNet) with a transfer learning approach which we use to produce an ensemble of protein family-specific models. We conduct an in-depth empirical study and provide the first guidelines on the minimum requirements for adopting a protein family-specific model. Our method also highlights the need for additional data, even in data-rich protein families. Our approach outperforms recent benchmarks on the DUD-E data set and an independent test set constructed from the ChEMBL database. Using a clustered cross-validation on DUD-E, we achieve an average AUC ROC of 0.92 and a 0.5% ROC enrichment factor of 79. This represents an improvement in early enrichment of over 75% compared to a recent machine learning benchmark. Our results demonstrate that the continued improvements in machine learning architecture for computer vision apply to structure-based virtual screening.

Protein structure and computational drug discovery.

The first protein structures revealed a complex web of weak interactions stabilising the three-dimensional shape of the molecule. Small molecule ligands were then found to exploit these same weak binding events to modulate protein function or act as substrates in enzymatic reactions. As the understanding of ligand-protein binding grew, it became possible to firstly predict how and where a particular small molecule might interact with a protein, and then to identify putative ligands for a specific protein site. Computer-aided drug discovery, based on the structure of target proteins, is now a well-established technique that has produced several marketed drugs. We present here an overview of the various methodologies being used for structure-based computer-aided drug discovery and comment on possible future developments in the field.

COMPUTER AIDED DRUG DESIGN: AN OVERVIEW

Discovery and development of a new drug is generally known as a very complex process which takes a lot of time and resources. So now a day’s computer aided drug design approaches are used very widely to increase the efficiency of the drug discovery and development course. Various approaches of CADD are evaluated as promising techniques according to their need, in between all these structure-based drug design and ligand-based drug design approaches are known as very efficient and powerful techniques in drug discovery and development. These both methods can be applied with molecular docking to virtual screening for lead identification and optimization. In the recent times computational tools are widely used in pharmaceutical industries and research areas to improve effectiveness and efficacy of drug discovery and development pipeline. In this article we give an overview of computational approaches, which is inventive process of finding novel leads and aid in the process of drug discovery and development research.
 Keywords: computer aided drug discovery, structure-based drug design, ligand-based drug design, virtual screening and molecular docking

Computer-aided drug discovery of Myc-Max inhibitors as potential therapeutics for prostate cancer

While Myc is an essential regulator of growth in normal cells, it is also frequently associated with cancer progression, therapy-resistance and lethal outcomes in most human cancers. In prostate cancer (PCa), Myc transcription factors are implicated in the pathogenesis and progression of the full spectrum of PCa, from adenocarcinoma to advanced castration-resistant and neuroendocrine phenotypes. Though a high-value therapeutic target, clinically approved anti-Myc drugs have yet to be discovered. To elicit its oncogenic effects, Myc must form a heterodimer with its partner Max, which together bind DNA and activate transcription of a spectrum of target genes that promote cell growth, proliferation, metabolism, and apoptosis while blocking differentiation. In this study, we identified a binding site on the DNA-binding domain of the structurally ordered Myc-Max complex and employed a computer-aided rational drug discovery approach to identify small molecules that effectively inhibit Myc-Max functionality. A large-scale virtual screening protocol implementing structure-based methodologies was utilized to select a set of top-ranked compounds that were subsequently evaluated experimentally and characterized mechanistically for their ability to inhibit Myc-Max transcriptional activity and subsequent downstream functions, to reduce viability in PCa cell lines, disrupt protein-DNA interactions and to induce apoptosis as their mechanism of action. Among compounds identified that effectively inhibit Myc-Max activity with low to mid-micromolar range potency and no or minimal generic cytotoxicity, VPC-70067, a close analog of the previously identified Myc inhibitor 10058-F4, served as proof-of-concept that our in silico drug discovery strategy performed as expected. Compound VPC-70063, of a chemically different scaffold, was the best performer in a panel of in vitro assays, and the forerunner for future hit-to-lead optimization efforts. These findings lay a foundation for developing more potent, specific and clinically optimized Myc-Max inhibitors that may serve as promising therapeutics, alone or in combination with current anti-cancer treatments, for treatment of specific phenotypes or heterogeneous tumors.

Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges.

Advanced mathematics, such as multiscale weighted colored subgraph and element specific persistent homology, and machine learning including deep neural networks were integrated to construct mathematical deep learning models for pose and binding affinity prediction and ranking in the last two D3R Grand Challenges in computer-aided drug design and discovery. D3R Grand Challenge 2 focused on the pose prediction, binding affinity ranking and free energy prediction for Farnesoid X receptor ligands. Our models obtained the top place in absolute free energy prediction for free energy set 1 in stage 2. The latest competition, D3R Grand Challenge 3 (GC3), is considered as the most difficult challenge so far. It has five subchallenges involving Cathepsin S and five other kinase targets, namely VEGFR2, JAK2, p38-α, TIE2, and ABL1. There is a total of 26 official competitive tasks for GC3. Our predictions were ranked 1st in 10 out of these 26 tasks.

Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery.

The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their “undruggable” binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.

Computer-aided drug discovery: Novel 3,9-disubstituted eudistomin U derivatives as potent antibacterial agents

Thirty-two new 3,9-disubstituted eudistomin U derivatives were designed and synthesized based on computer-aided drug discovery (CADD). Sixteen 3,9-disubstituted eudistomin U derivatives (6a–6p) have exhibited potent antibacterial activity. Specially, the most active compound 6p displayed better activity than commercial drugs fosfomycin sodium, ciprofloxacin and propineb, with a peak minimum inhibitory concentration (MIC) of 1.5625 μmol/L. The antibacterial mechanism indicated that these compounds could exert bactericidal effect by damaging bacterial cell membrane and disrupting the function of DNA gyrase.

Exploring Cryptic Pockets Formation in Targets of Pharmaceutical Interest with SWISH.

Cryptic (hidden) pockets are sites that are not visible on unliganded target proteins' structures and only become apparent when a ligand binds. They might provide a valid alternative to classical binding sites in otherwise "undruggable" targets, but their hidden nature makes it difficult to use standard structure-based or computer-aided drug discovery approaches. Our group recently developed a Hamiltonian replica-exchange method (sampling water interfaces through scaled Hamiltonians or SWISH) that improves the sampling of hydrophobic cavities by scaling the interactions between water molecules and protein atoms. Here, we discuss further improvements to SWISH and its combination with fragment probe simulations. We tested the robustness and general applicability of the improved approach in a variety of pharmaceutically relevant targets. The chosen proteins: NPC2, p38α, LfrR, and hPNMT, represent a set of diversified and interesting targets harboring nontrivial cryptic binding sites. In all cases, the updated version of our algorithm efficiently explored the cryptic sites.

Targeting Cysteine Proteases from Plasmodium falciparum: A General Overview, Rational Drug Design and Computational Approaches for Drug Discovery.

The Plasmodium falciparum cysteine proteases, also known as falcipains, are involved in different erythrocytic cycle processes of the malaria parasite, e.g. hydrolysis of host haemoglobin, erythrocyte invasion, and erythrocyte rupture. With the biochemical characterization of four falcipains so far, FP-2 (falcipain-2) and FP-3 (falcipain-3), members of the papain-like CAC1 family, are essential haemoglobinases. They could therefore be referred to as potential anti-malarial drug targets in the search for novel therapies, which could ease the burden caused by the increasing resistance to current antimalarial drugs. This review provides a summary of the most important results, highlighting the drug design approaches essential for the understanding of the mechanism of inhibition and discovery of inhibitors against cysteine proteases from P. falciparum. Rational and computer-aided drug discovery approaches for the design of promising falcipain inhibitors are described herein, with a focus on a variety of structure-based and ligand-based modeling approaches. Moreover, the key features of ligand recognition against these targets are emphasized. This review would be of interest to scientists engaged in the development of drug design strategies to target the cysteine proteases, FP-2 and FP-3.

Computer-Aided Drug Discovery: Molecular Docking of Diminazene Ligands to DNA Minor Groove

The reported project-based laboratory unit introduces upper-division undergraduate students to the basics of computer-aided drug discovery as a part of a computational chemistry laboratory course. The students learn to perform model binding of organic molecules (ligands) to the DNA minor groove with computer-aided drug discovery (CADD) tools. The purpose of this laboratory unit is to dock diminazene and its derivatives to DNA, to estimate their binding energies, and to elucidate details of DNA–ligand intermolecular interactions. The computational procedure also helps to screen binding capabilities of DNA ligands.

Rational Design, Synthesis and Biological Evaluation of Pyrimidine-4,6-diamine derivatives as Type-II inhibitors of FLT3 Selective Against c-KIT

FMS-like Tyrosine Kinase 3 (FLT3) is a clinically validated target for acute myeloid leukemia (AML). Inhibitors targeting FLT3 have been evaluated in clinical studies and have exhibited potential to treat FLT3-driven AML. A frequent, clinical limitation is FLT3 selectivity, as concomitant inhibition of FLT3 and c-KIT is thought to cause dose-limiting myelosuppression. Through a rational design approach, novel FLT3 inhibitors were synthesized employing a pyridine/pyrimidine warhead. The most potent compound identified from the studies is compound 13a, which exhibited an IC50 value of 13.9 ± 6.5 nM against the FLT3 kinase with high selectivity over c-KIT. Mechanism of action studies suggested that 13a is a Type-II kinase inhibitor, which was also supported through computer aided drug discovery (CADD) efforts. Cell-based assays identified that 13a was potent on a variety of FLT3-driven cell lines with clinical relevance. We report herein the discovery and therapeutic evaluation of 4,6-diamino pyrimidine-based Type-II FLT3 inhibitors, which can serve as a FLT3-selective scaffold for further clinical development.