Sci Forschen Open HUB for Scientific Research Journal of Drug Research and Development Open Access Copyright: © 2017 Chemi G, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Volume: 3.1 Mini Review Breakthroughs in Computational Approaches for Drug Discovery Giulia Chemi and Simone Brogi* European Research Centre for Drug Discovery and Development (NatSynDrugs) and Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Via Aldo Moro 2, 53100 Siena, Italy Received date: 23 Nov 2016; Accepted date: 19 Jan 2017; Published date: 25 Jan 2017. Citation: Chemi G, Brogi S (2017) Breakthroughs in Computational Approaches for Drug Discovery. J Drug Res Dev 3(1): doi http://dx.doi. org/10.16966/2470-1009.129 Copyright: © 2017 Chemi G, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. * Corresponding author: Simone Brogi, European Research Centre for Drug Discovery and Development and Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Via Aldo Moro 2, 53100 Siena, Italy, Tel: +39-0577-234389; E-mail: simonebrogi1976@hotmail.com; brogi32@unisi.it In silico methodologies have become a pivotal part of the modern drug discovery process. Since their origin, computational techniques demonstrated to accelerate hit selection for a given drug target, and to signiicantly contribute to multiple stages of drug discovery (i.e. drug optimization) [1]. Accordingly, in silico drug design and discovery is in a state of constant and rapid development due to: (i) progress in the computer science which has led to the generation of powerful and afordable supercomputers, proliferation of available online tools, sotware and databases and development of more reliable algorithms; (ii) development of new experimental procedures for the characterization of biological targets (i.e. X-ray crystallography and NMR spectroscopy); (iii) the greater awareness of the molecular basis of drug action. Herein we analyzed the most relevant computer aided drug design (CADD) breakthroughs. A variety of computational approaches with diverse potential applications along the drug discovery process (Figure 1) will be discussed and the last improvements of the in silico tools and methodologies examined. Ligand-based and Structure-based Methods in Drug- Design Pharmacophore modeling, three-dimensional quantitative structure- activity relationships (3D-QSAR), Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) still remain the ligand-based (LB) methods of choice for fast virtual screening (VS) procedures. hey are particularly powerful when the three-dimensional (3D) structure of the investigated protein is unknown [2-4]. VS is routinely employed by academia and pharmaceutical companies to identify novel chemical entities using public (e.g. ZINC database [5]), commercial or proprietary 3D-databases, with the possibility to screen billion of compounds in a short time, in order to reduce drug discovery costs [6]. he large amounts of available positive information (i.e. biological and structural data) allow the use of large dataset of known characterized compounds also for the development of 3D-QSAR models. hese are crucial information for relating the structural and/or physicochemical properties of compounds to their activities in order to obtain more robust statistical in silico models for predicting activities of novel chemical entities [7]. CoMFA and CoMSIA are powerful tools to generate 3D-QSAR models to correlate the biological activity of a set of molecules and their 3D shape, electrostatic and hydrogen bonding characteristics. his correlation is derived from a series of superimposed conformations, one for each molecule in the set. he molecular ields around each conformation are calculated and the resulting 3D models can be used in VS protocols by using for example SYBYL-X Suite (Certara USA, Inc., Princeton, New Jersey, NJ). Accordingly, the expertise in the generation of QSAR models and the development of statistical packages employing public available databases (considering theoretical or experimental data), made possible the realization of revised structure-relationships models. Below are reported important examples: (i) 3D quantitative structure-selectivity relationships (3D-QSSRs) models [8,9]. In this approach, by means of Phase sotware (Schrödinger, LLC, New York, NY), the classical 3D-QSAR was slightly modiied taking as dependent variable the selectivity index of the compounds and not the activity toward a selected target (Cannabinoid Receptor 2). his allows the development of a comprehensive structure-selectivity instead of structure-activity model. he obtained model was successfully used to rationally design highly selective ligands for the Cannabinoid Receptor 2 [8,10]; (ii) Multi-target quantitative structure-activity relationships (mtQSARs) models. hese are useful for simultaneously estimating activities against diferent biological targets using big and unrelated datasets of compounds [11]; (iii) 3D quantitative structure-properties relationships (3D-QSPRs) models [12]. In detail, QSPR can be clustered in various sub-ields including quantitative structure -reactivity (QSRRs), -toxicity (QSTRs), -chromatography (QSCRs), -biodegradability (QSBRs), -electrochemistry (QSERs) relationships [12]. During the last decade, many scientiic contributions appeared in the literature reporting improved QSAR methodologies. hese advancements in structure-relationships models are extremely useful for rational drug design and for predicting ligands’ undesirable efects such as hERG K + channel ainity. hERG K + channel is a well-known antitarget responsible for cardiotoxic efects when targeted by centrally active drugs. In fact, the interaction of small molecules with hERG K + channel is one of the major issues encountered by the pharmaceutical companies related to the drug development process. In the recent years several marketed drugs including astemizole, droperidol, terfenadine, lidolazine, sertindole, cisapride, and chlorpromazine have been withdrawn due to their relevant activity on hERG K + channel. In this context, the generation of an adequate 3D-QSAR model based on hERG K + channel blockers can assist the rational design of new potentially bioactive drugs devoid of hERG K + channel ainity. When the information of the 3D structure of the targets in complex with ligands are known, structure-based (SB) drug design approaches are useful for deriving SB pharmacophore models including excluded volumes (3D space portions in which the ligand cannot be located). he most commonly used sotware for generating SB pharmacophore models ISSN 2470-1009