Computer-aided Drug Discovery Research Articles

Documenting and harnessing the biological potential of molecules in Distributed Drug Discovery (D3) virtual catalogs.

Virtual molecular catalogs have limited utility if member compounds are (i) difficult to synthesize or (ii) unlikely to have biological activity. The Distributed Drug Discovery (D3) program addresses the synthesis challenge by providing scientists with a free virtual D3 catalog of 73,024 easy-to-synthesize N-acyl unnatural α-amino acids, their methyl esters, and primary amides. The remaining challenge is to document and exploit the bioactivity potential of these compounds. In the current work, a search process is described that retrospectively identifies all virtual D3 compounds classified as bioactive hits in PubChem-cataloged experimental assays. The results provide insight into the broad range of drug-target classes amenable to inhibition and/or agonism by D3-accessible molecules. To encourage computer-aided drug discovery centered on these compounds, a publicly available virtual database of D3 molecules prepared for use with popular computer docking programs is also presented.

Best Practices of Computer-Aided Drug Discovery: Lessons Learned from the Development of a Preclinical Candidate for Prostate Cancer with a New Mechanism of Action.

Small-molecule drug design is a complex and iterative decision-making process relying on pre-existing knowledge and driven by experimental data. Low-molecular-weight chemicals represent an attractive therapeutic option, as they are readily accessible to organic synthesis and can easily be characterized.1 Their potency as well as pharmacokinetic and pharmacodynamic properties can be systematically and rationally investigated and ultimately optimized via expert science behind medicinal chemistry and methods of computer-aided drug design (CADD). In recent years, significant advances in molecular modeling techniques have afforded a variety of tools to effectively identify potential binding pockets on prospective targets, to map key interactions between ligands and their binding sites, to construct and assess energetics of the resulting complexes, to predict ADMET properties of candidate compounds, and to systematically analyze experimental and computational data to derive meaningful structure-activity relationships leading to the creation of a drug candidate. This Perspective describes a real case of a drug discovery campaign accomplished in a relatively short time with limited resources. The study integrated an arsenal of available molecular modeling techniques with an array of experimental tools to successfully develop a novel class of potent and selective androgen receptor inhibitors with a novel mode of action. It resulted in the largest academic licensing deal in Canadian history, totaling $142M. This project exemplifies the importance of team science, an integrative approach to drug discovery, and the use of best practices in CADD. We posit that the lessons learned and best practices for executing an effective CADD project can be applied, with similar success, to many drug discovery projects in both academia and industry.

Inhibition of serum and glucocorticoid regulated kinase-1 as novel therapy for cardiac arrhythmia disorders

Alterations in sodium flux (INa) play an important role in the pathogenesis of cardiac arrhythmias and may also contribute to the development of cardiomyopathies. We have recently demonstrated a critical role for the regulation of the voltage-gated sodium channel NaV1.5 in the heart by the serum and glucocorticoid regulated kinase-1 (SGK1). Activation of SGK1 in the heart causes a marked increase in both the peak and late sodium currents leading to prolongation of the action potential duration and an increased propensity to arrhythmia. Here we show that SGK1 directly regulates NaV1.5 channel function, and genetic inhibition of SGK1 in a zebrafish model of inherited long QT syndrome rescues the long QT phenotype. Using computer-aided drug discovery coupled with in vitro kinase assays, we identified a novel class of SGK1 inhibitors. Our lead SGK1 inhibitor (5377051) selectively inhibits SGK1 in cultured cardiomyocytes, and inhibits phosphorylation of an SGK1-specific target as well as proliferation in the prostate cancer cell line, LNCaP. Finally, 5377051 can reverse SGK1’s effects on NaV1.5 and shorten the action potential duration in induced pluripotent stem cell (iPSC)-derived cardiomyocytes from a patient with a gain-of-function mutation in Nav 1.5 (Long QT3 syndrome). Our data suggests that SGK1 inhibitors warrant further investigation in the treatment of cardiac arrhythmias.

In silico discovery and in vitro activity of inhibitors against Mycobacterium tuberculosis 7,8-diaminopelargonic acid synthase (Mtb BioA).

Computer-aided drug discovery and development approaches such as virtual screening, molecular docking, and in silico drug property calculations have been utilized in this effort to discover new lead compounds against tuberculosis. The enzyme 7,8-diaminopelargonic acid aminotransferase (BioA) in Mycobacterium tuberculosis (Mtb), primarily involved in the lipid biosynthesis pathway, was chosen as the drug target due to the fact that humans are not capable of synthesizing biotin endogenously. The computational screening of 4.5 million compounds from the Enamine REAL database has ultimately yielded 45 high-scoring, high-affinity compounds with desirable in silico absorption, distribution, metabolism, excretion, and toxicity properties. Seventeen of the 45 compounds were subjected to bioactivity validation using the resazurin microtiter assay. Among the 4 actives, compound 7 ((Z)-N-(2-isopropoxyphenyl)-2-oxo-2-((3-(trifluoromethyl)cyclohexyl)amino)acetimidic acid) displayed inhibitory activity up to 83% at 10 μg/mL concentration against the growth of the Mtb H37Ra strain.

CADD Modeling of Multi-Target Drugs Against Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disorder that is described by multiple factors linked with the progression of the disease. The currently approved drugs in the market are not capable of curing AD; instead, they merely provide symptomatic relief. Development of multi-target directed ligands (MTDLs) is an emerging strategy for improving the quality of the treatment against complex diseases like AD. Polypharmacology is a branch of pharmaceutical sciences that deals with the MTDL development. In this mini-review, we have summarized and discussed different strategies that are reported in the literature to design MTDLs for AD. Further, we have discussed the role of different in silico techniques and online resources in computer-aided drug discovery (CADD), for designing or identifying MTDLs against AD.

Towards Dynamic Pharmacophore Models by Coarse Grain Molecular Dynamics

Pharmacophore models play a key role in computer aided drug discovery e.g. in virtual screening of chemical databases, de novo drug design, and lead optimization. Structure-based methods for developing pharmacophore models are of particular importance, and there have been a number of studies combining such methods with the use of molecular dynamics (MD) simulations to model protein flexibility. At the same time, ongoing developments in multi-scale simulations, in which atomistic and CG MD simulations are combined in a sequential fashion, have been successfully used to explore the interactions of proteins with membranes and their lipids [1,2]. Here we describe the use of CG-MD simulations to explore the interactions of CG-particle probes with proteins, combined with cavity detection methods, in order to identify potential protein binding sites. Using cyclin dependent kinase 2 (CDK2) as a test case, we demonstrate the potential of this approach for identification of ligand binding sites, and as part of an overall process of structure-based development of pharmacophore models.

CADD medicine: design is the potion that can cure my disease.

The acronym "CADD" is often used interchangeably to refer to "Computer Aided Drug Discovery" and "Computer Aided Drug Design". While the former definition implies the use of a computer to impact one or more aspects of discovering a drug, in this paper we contend that computational chemists are most effective when they enable teams to apply true design principles as they strive to create medicines to treat human disease. We argue that teams must bring to bear multiple sub-disciplines of computational chemistry in an integrated manner in order to utilize these principles to address the multi-objective nature of the drug discovery problem. Impact, resourcing principles, and future directions for the field are also discussed, including areas of future opportunity as well as a cautionary note about hype and hubris.

Computer-Aided Drug Discovery from Marine Compounds: Identification of the Three-Dimensional Structural Features Responsible for Antimalarial Activity.

An integrated computational approach, based on molecular dynamics/mechanics, semi-empirical, and DFT calculations as well as dynamic docking studies, has been employed to gain insight into the mechanism of action of new antimalarial agents characterized by the scaffold of the marine compounds plakortin and aplidinone. The results of this approach show that these molecules, after interaction with Fe(II), likely coming from the heme molecule, give rise to the formation of radical species, that should represent the toxic intermediates responsible for subsequent reactions leading to plasmodium death. The three-dimensional structural requirements necessary for the activity of these new classes of antimalarial agents have been identified and discussed throughout the chapter.

High-Throughput Screening Assay Datasets from the PubChem Database

Availability of high-throughput screening (HTS) data in the public domain offers great potential to foster development of ligand-based computer-aided drug discovery (LB-CADD) methods crucial for drug discovery efforts in academia and industry. LB-CADD method development depends on high-quality HTS assay data, i.e., datasets that contain both active and inactive compounds. These active compounds are hits from primary screens that have been tested in concentration-response experiments and where the target-specificity of the hits has been validated through suitable secondary screening experiments. Publicly available HTS repositories such as PubChem often provide such data in a convoluted way: compounds that are classified as inactive need to be extracted from the primary screening record. However, compounds classified as active in the primary screening record are not suitable as a set of active compounds for LB-CADD experiments due to high false-positive rate. A suitable set of actives can be derived by carefully analysing results in often up to five or more assays that are used to confirm and classify the activity of compounds. These assays, in part, build on each other. However, often not all hit compounds from the previous screen have been tested. Sometimes a compound can be classified as 'active', though its meaning is 'inactive' on the target of interest as it is 'active' on a different target protein. Here, a curation process of hierarchically related confirmatory screens is illustrated based on two specifically chosen protein use-cases. The subsequent re-upload procedure into PubChem is described for the findings of those two scenarios. Further, we provide nine publicly accessible high quality datasets for future LB-CADD method development that provide a common baseline for comparison of future methods to the scientific community. We also provide a protocol researchers can follow to upload additional datasets for benchmarking.

Computational chemistry at Janssen

Computer-aided drug discovery activities at Janssen are carried out by scientists in the Computational Chemistry group of the Discovery Sciences organization. This perspective gives an overview of the organizational and operational structure, the science, internal and external collaborations, and the impact of the group on Drug Discovery at Janssen.

Computational methods in drug discovery.

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein–ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

Identification of novel drug candidate against Mycobacterium Tuberculosis InhA protein through Computer aided drug discovery

Tuberculosis is a common and often deadly infectious disease caused by Mycobacteria. WHO estimates that around eight million people are suffering from tuberculosis every year. The Administration of antibiotics fails due to the multi-drug resistant capability of Mycobacterium. Traditional methods require time and huge investment. Therefore, it is essential to provide an alternative strategy to treat tuberculosis disease. The present study conducted molecular docking against InhA protein, which is a potential target of tuberculosis disease. After docking with 1200 ligand molecule, 30 hits were selected based on the GOLD score. Finally, five potential hits were identified after redocking. The hydrogen bonding pattern, Lipinski’s rule, and drug likeliness were also checked for their potentiality as suitable drug candidates. We believe that our study will facilitate the selection of InhA as a potential target and selected hits as successful drug candidates for tuberculosis diseases. (Less)

Computer-aided drug discovery research at a global contract research organization

Computer-aided drug discovery started at Albany Molecular Research, Inc in 1997. Over nearly 20years the role of cheminformatics and computational chemistry has grown throughout the pharmaceutical industry and at AMRI. This paper will describe the infrastructure and roles of CADD throughout drug discovery and some of the lessons learned regarding the success of several methods. Various contributions provided by computational chemistry and cheminformatics in chemical library design, hit triage, hit-to-lead and lead optimization are discussed. Some frequently used computational chemistry techniques are described. The ways in which they may contribute to discovery projects are presented based on a few examples from recent publications.

Computational chemistry and computer-aided drug discovery: Part II.

Computational chemistry and computer-aided drug discovery: Part II.

Protein Structure Network-based Drug Design.

Although structure-based drug design (SBDD) has become an indispensable tool in drug discovery for a long time, it continues to pose major challenges to date. With the advancement of "omics" techniques, systems biology has enriched SBDD into a new era, called polypharmacology, in which multi-targets drug or drug combination is designed to fight complex diseases. As a preliminary tool in systems biology, protein structure networks (PSNs) treat a protein as a set of residues linked by edges corresponding to the intramolecular interactions existing in folded structures between the residues. The PSN offers a computationally efficient tool to study the structure and function of proteins, and thus may facilitate structurebased drug design. Herein, we provide an overview of recent advances in PSNs, from predicting functionally important residues, to charactering protein-protein interactions and allosteric communication paths. Furthermore, we discuss potential pharmacological applications of PSN concepts and tools, and highlight the application to two families of drug targets, GPCRs and Hsp90. Although the application of PSNs as a framework for computer-aided drug discovery has been limited to date, we put forward the potential utility value in the near future and propose the PSNs could also serve as a new tool for polypharmacology research.

Docking-undocking combination applied to the D3R Grand Challenge 2015.

Novel methods for drug discovery are constantly under development and independent exercises to test and validate them for different goals are extremely useful. The drug discovery data resource (D3R) Grand Challenge 2015 offers an excellent opportunity as an external assessment and validation experiment for Computer-Aided Drug Discovery methods. The challenge comprises two protein targets and prediction tests: binding mode and ligand ranking. We have faced both of them with the same strategy: pharmacophore-guided docking followed by dynamic undocking (a new method tested experimentally here) and, where possible, critical assessment of the results based on pre-existing information. In spite of using methods that are qualitative in nature, our results for binding mode and ligand ranking were amongst the best on Hsp90. Results for MAP4K4 were less positive and we track the different performance across systems to the level of previous knowledge about accessible conformational states. We conclude that docking is quite effective if supplemented by dynamic undocking and empirical information (e.g. binding hot spots, productive protein conformations). This setup is well suited for virtual screening, a frequent application that was not explicitly tested in this edition of the D3R Grand Challenge 2015. Protein flexibility remains as the main cause for hard failures.

Dissecting the Influence of Protein Flexibility on the Location and Thermodynamic Profile of Explicit Water Molecules in Protein-Ligand Binding.

Explicit water molecules in the binding site of proteins play a crucial role for protein-ligand association. Recent advances in computer-aided drug discovery methodology allow for an accurate prediction of the localized position and thermodynamic profile of water molecules (i.e., hydration sites) in the binding site. The underlying calculations are based on MD simulations of explicit water molecules in a restrained protein structure. However, the ligand-binding process is typically associated with protein conformational change that influences the position and thermodynamic properties of the hydration site. In this manuscript, we present the developments of two methods to incorporate the influence of protein conformational change on hydration sites either by following the conformational transition step-by-step (method I) or to match the hydration sites of the two transition end states using local coordinate systems (method II). Using these methods, we highlight the difference in the estimated protein desolvation free energy with and without inclusion of protein flexibility. To the best of our knowledge, this is the first study that explicitly studies the influence of protein conformational change on the position and thermodynamic profiles of water molecules and provides methodology to incorporate protein flexibility into the estimation of the desolvation free energy.

Use of Integrated Computational Approaches in the Search for New Therapeutic Agents.

Computer-aided drug discovery plays a strategic role in the development of new potential therapeutic agents. Nevertheless, the modeling of biological systems still represents a challenge for computational chemists and at present a single computational method able to face such challenge is not available. This prompted us, as computational medicinal chemists, to develop in-house methodologies by mixing various bioinformatics and computational tools. Importantly, thanks to multi-disciplinary collaborations, our computational studies were integrated and validated by experimental data in an iterative process. In this review, we describe some recent applications of such integrated approaches and how they were successfully applied in i) the search of new allosteric inhibitors of protein-protein interactions and ii) the development of new redox-active antimalarials from natural leads.

Computational Approaches for the Discovery of Human Proteasome Inhibitors: An Overview.

Proteasome emerged as an important target in recent pharmacological research due to its pivotal role in degrading proteins in the cytoplasm and nucleus of eukaryotic cells, regulating a wide variety of cellular pathways, including cell growth and proliferation, apoptosis, DNA repair, transcription, immune response, and signaling processes. The last two decades witnessed intensive efforts to discover 20S proteasome inhibitors with significant chemical diversity and efficacy. To date, the US FDA approved to market three proteasome inhibitors: bortezomib, carfilzomib, and ixazomib. However new, safer and more efficient drugs are still required. Computer-aided drug discovery has long being used in drug discovery campaigns targeting the human proteasome. The aim of this review is to illustrate selected in silico methods like homology modeling, molecular docking, pharmacophore modeling, virtual screening, and combined methods that have been used in proteasome inhibitors discovery. Applications of these methods to proteasome inhibitors discovery will also be presented and discussed to raise improvements in this particular field.

Software Infrastructure for Computer-aided Drug Discovery and Development, a Practical Example with Guidelines.

In the field of Computer-Aided Drug Discovery and Development (CADDD) the proper software infrastructure is essential for everyday investigations. The creation of such an environment should be carefully planned and implemented with certain features in order to be productive and efficient. Here we describe a solution to integrate standard computational services into a functional unit that empowers modelling applications for drug discovery. This system allows users with various level of expertise to run in silico experiments automatically and without the burden of file formatting for different software, managing the actual computation, keeping track of the activities and graphical rendering of the structural outcomes. To showcase the potential of this approach, performances of five different docking programs on an Hiv-1 protease test set are presented.