Computational Approaches for Drug Discovery

British Journal of Pharmacology, 2007

Pharmacology over the past 100 years has had a rich tradition of scientists with the ability to form qualitative or semiquantitative relations between molecular structure and activity in cerebro. To test these hypotheses they have consistently used traditional pharmacology tools such as in vivo and in vitro models. Increasingly over the last decade however we have seen that computational (in silico) methods have been developed and applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, pharmacophores, homology models and other molecular modeling approaches, machine learning, data mining, network analysis tools and data analysis tools that use a computer. In silico methods are primarily used alongside the generation of in vitro data both to create the model and to test it. Such models have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The aim of this review is to illustrate some of the in silico methods for pharmacology that are used in drug discovery. Further applications of these methods to specific targets and their limitations will be discussed in the second accompanying part of this review.

Molecules

In this paper we review the current status of high-performance computing applications in the general area of drug discovery. We provide an introduction to the methodologies applied at atomic and molecular scales, followed by three specific examples of implementation of these tools. The first example describes in silico modeling of the adsorption of small molecules to organic and inorganic surfaces, which may be applied to drug delivery issues. The second example involves DNA translocation through nanopores with major significance to DNA sequencing efforts. The final example offers an overview of computer-aided drug design, with some illustrative examples of its usefulness.

British Journal of Pharmacology, 2007

Computational (in silico) methods have been developed and widely applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, similarity searching, pharmacophores, homology models and other molecular modeling, machine learning, data mining, network analysis tools and data analysis tools that use a computer. Such methods have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The first part of this review discussed the methods that have been used for virtual ligand and target-based screening and profiling to predict biological activity. The aim of this second part of the review is to illustrate some of the varied applications of in silico methods for pharmacology in terms of the targets addressed. We will also discuss some of the advantages and disadvantages of in silico methods with respect to in vitro and in vivo methods for pharmacology research. Our conclusion is that the in silico pharmacology paradigm is ongoing and presents a rich array of opportunities that will assist in expediating the discovery of new targets, and ultimately lead to compounds with predicted biological activity for these novel targets.

PC supported medication revelation/outline techniques have assumed a noteworthy part in the improvement of remedially critical little atoms for more than three decades. These techniques are extensively delegated either structure-based or ligand-based strategies. Structure-based strategies are on a fundamental level similar to high-throughput screening in that both target and ligand structure data is basic. Structure-based methodologies incorporate ligand docking, pharmaco phore , and ligand plan techniques. The article examines hypothesis behind the most imperative techniques and late effective applications. Ligand-based strategies utilize just ligand data for anticipating movement relying upon its closenes s /uniquenes s to beforehand known dynamic ligands.

OA Drug Design and Delivery, 2013

Drug discovery and development is an intense, lengthy and an inter-disciplinary venture. Recently, a trend towards the use of in-silico chemistry and molecular modeling for computer-aided drug design has gained significant momentum. In-silico drug design skills are used in nanotechnology, molecular biology, biochemistry etc. The main benefit of the in-silico drug design is cost effective in research and development of drugs. There are wide ranges of software are used in in-silico drug design, Grid computing, window based general PBPK/PD modeling software, PKUDDS for structure based drug design, APIS, JAVA, Perl and Python, insilico drug design as well as software including software libraries. There are different techniques used in in-silico drug design visualization, homology, molecular dynamic, energy minimization molecular docking and QSAR etc. In-silico drug design can take part considerably in all stages of drug development from the preclinical discovery stage to late stage clinical development. Its exploitation in drug development helps in the selection of only a potent lead molecule and may thus thwart the late stage clinical failures; thereby a major diminution in cost can be achieved. This article gives an insight to all the aspects of in-silico drug design; its potential, drivers, current development and the future prospects.

Quantitative Structure-Activity Relationships in Drug Design, Predictive Toxicology, and Risk Assessment

Although almost fully automated, the discovery of novel, effective, and safe drugs is still a long-term and highly expensive process. Consequently, the need for fleet, rational, and cost-efficient development of novel drugs is crucial, and nowadays the advanced in silico drug design methodologies seem to effectively meet these issues. The aim of this chapter is to provide a comprehensive overview of some of the current trends and advances in the in silico design of novel drug candidates with a special emphasis on 6-fluoroquinolone (6-FQ) antibacterials as potential novel Mycobacterium tuberculosis DNA gyrase inhibitors. In particular, the chapter covers some of the recent aspects of a wide range of in silico drug discovery approaches including multidimensional machine-learning methods, ligand-based and structurebased methodologies, as well as their proficient combination and integration into an intelligent virtual screening protocol for design and optimization of novel 6-FQ analogs.

Journal of current pharma research, 2012

Drug discovery is a critical issue in the pharmaceutical research as it is a very cost effective and time consuming process to produce new drug candidate. So, there is number of computational advances which have significant impact in the field of computer aided drug design over the last several years. These advances can be grouped into three basic areas: conformational modeling (of small molecules, macromolecules and their complexes), property modeling (of physical, biological and chemical properties) and molecular design (to optimize physical, biological or chemical properties). Hence, computational approaches have given a tremendous opportunity to pharmaceutical companies to identify new potential drug targets which in turn affect the success and time of performing clinical trials for discovering new drug targets.

The field of computer aided drug design and discovery (CADDD) is a rapidly growing area that have seen many successes in the last few years. Many giant pharmaceutical companies, in addition to academia, adopt CADDD for drug lead discovery. The explosion of structural informatics, genomics and proteomic plays a major role in leading the efforts towards modern era drug discovery and development. This review discusses the recent advances in two of the major vehicles of CADDD, Molecular modeling and docking and some of the success stories accomplished by both academia and pharmaceutical industry using molecular modeling and docking towards discovery of new drug leads.

Computational methods play a central role in modern drug discovery process. It includes the design and management of small molecule libraries, initial hit identification through virtual screening, optimization of the affinity as well as selectivity of hits and improving the physicochemical properties of the lead compounds. In this review article, computational drug designing approaches have been elucidated and discussed. The key considerations and guidelines for virtual chemical library design and whole drug discovery process. Traditional approach for discovery of a new drug is a costly and time consuming affair besides not being so productive. A number of potential reasons witness choosing the In-silico method of drug design to be a more wise and productive approach. There is a general perception that applied science has not kept pace with the advances of basic science. Therefore, there is a need for the use of alternative tools to get answers on efficacy and safety faster, with more certainty and at lower cost. In-silico drug design can play a significant role in all stages of drug development from the initial lead designing to final stage clinical development.

Journal of Microbiology, 2020

Due to accumulating protein structure information and advances in computational methodologies, it has now become possible to predict protein-compound interactions. In biology, the classic strategy for drug discovery has been to manually screen multiple compounds (small scale) to identify potential drug compounds. Recent strategies have utilized computational drug discovery methods that involve predicting target protein structures, identifying active sites, and finding potential inhibitor compounds at large scale. In this protocol article, we introduce an in silico drug discovery protocol. Since multi-drug resistance of pathogenic bacteria remains a challenging problem to address, UDP-N-acetylmuramate-L-alanine ligase (murC) of Acinetobacter baumannii was used as an example, which causes nosocomial infection in hospital setups and is responsible for high mortality worldwide. This protocol should help microbiologists to expand their knowledge and research scope.

Drug Discovery Today, 2012

Quantitative structure-activity relationship (QSAR) methods and related approaches have been used to investigate the molecular features that influence the absorption, distribution, metabolism, excretion and toxicity (ADMET) of drugs. As the three-dimensional structures of several major ADMET proteins become available, structure-based (docking-scoring) computations can be carried out to complement or to go beyond QSAR studies. Applying docking-scoring methods to ADMET proteins is a challenging process because they usually have a large and flexible binding cavity; however, promising results relating to metabolizing enzymes have been reported. After reviewing current trends in the field we applied structure-based methods in the context of receptor flexibility in a case study involving the phase II metabolizing sulfotransferases. Overall, the explored concepts and results suggested that structure-based ADMET profiling will probably join the mainstream during the coming years.

GSC Biological and Pharmaceutical Sciences

The process of discovering and developing a new medication is often seen as a lengthy and expensive endeavors. As a result, computer-aided drug design methods are now frequently utilized to improve the efficiency of the drug discovery and development process. Various CADD approaches are regarded as potential techniques based on their needs; nevertheless, structure-based drug design and ligand-based drug design approaches are well-known as highly efficient and powerful strategies in drug discovery and development. Both of these approaches may be used in conjunction with molecular docking to conduct virtual screening for the purpose of identifying and optimizing leads. In recent years, computational tools have become increasingly popular in the pharmaceutical industry and academic fields as a means of improving the efficiency and effectiveness of the drug discovery and development pipeline. In this post, we'll go over computational methods, which are a creative way of discovering ...

Infectious Disorders - Drug Targets(formerly Current Drug Targets - Infectious Disorders), 2009

The design of new medications is an intensive, time-consuming and costly process. Over the years, a rational approach that exploits the structural knowledge of a biological target has led to many successes. This procedure can be expedited using computer-aided modelling techniques.

Molecules, 2022

The conventional drug discovery approach is an expensive and time-consuming process, but its limitations have been overcome with the help of mathematical modeling and computational drug design approaches. Previously, finding a small molecular candidate as a drug against a disease was very costly and required a long time to screen a compound against a specific target. The development of novel targets and small molecular candidates against different diseases including emerging and reemerging diseases remains a major concern and necessitates the development of novel therapeutic targets as well as drug candidates as early as possible. In this regard, computational and mathematical modeling approaches for drug development are advantageous due to their fastest predictive ability and cost-effectiveness features. Computer-aided drug design (CADD) techniques utilize different computer programs as well as mathematics formulas to comprehend the interaction of a target and drugs. Traditional methods to determine small-molecule candidates as a drug have several limitations, but CADD utilizes novel methods that require little time and accurately predict a compound against a specific disease with minimal cost. Therefore, this review aims to provide a brief insight into the mathematical modeling and computational approaches for identifying a novel target and small molecular candidates for curing a specific disease. The comprehensive review mainly focuses on biological target prediction, structure-based and ligand-based drug design methods, molecular docking, virtual screening, pharmacophore modeling, quantitative structureactivity relationship (QSAR) models, molecular dynamics simulation, and MM-GBSA/MM-PBSA approaches along with valuable database resources and tools for identifying novel targets and therapeutics against a disease. This review will help researchers in a way that may open the road for the development of effective drugs and preventative measures against a disease in the future as early as possible.

Enzyme Engineering, 2014

Use of Computational (in silico) methods are widely applied in drug discovery. In drug discovery process, identification of the suitable drug target is the first and foremost task. These targets are biomolecules which mainly include DNA, RNA and proteins (such as receptors, transporters, enzymes and ion channels). Validation of such targets is necessary to exhibit a sufficient level of 'confidence' and to know their pharmacological relevance to the disease under investigation. The aim of this mini-review is to illustrate some of the in silico methods that are used in drug discovery, and to describe the applications of these computational methods.