Computational medicinal chemistry

Abstract

Future Medicinal ChemistryVol. 3, No. 4 Special Focus: Computational Chemistry - ForewordFree AccessComputational medicinal chemistryGisbert SchneiderGisbert SchneiderGuest Editor, Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, 8093 Zürich, Switzerland. Search for more papers by this authorEmail the corresponding author at gisbert.schneider@pharma.ethz.chPublished Online:31 Mar 2011https://doi.org/10.4155/fmc.11.10AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinkedInReddit The contributions to this series of thematic issues provide an insight into an emerging and most exciting area of drug discovery, while highlighting success stories as well as challenges for computational models in medicinal chemistry.In 1997, Lemont B Kier stated that, “It is no longer just sufficient to synthesize and test; experiments are played out in silico with prediction, classification and visualization being the necessary tools of medicinal chemistry” [1]. Without doubt, computer-based simulation of biochemical processes and molecular model building will increasingly drive molecular design and decision making in drug discovery. This future has already begun, and there is ample evidence that computational medicinal chemistry has become an important pillar of modern drug research.This compilation of thematic issues is specially devoted to this key topic. The subsequent issues within this thematic series will be published over the coming months. Contributions include authoritative review articles as well as original studies highlighting current research activities in the field, recent pivotal developments and likely future trends.The field of computational medicinal chemistry comprises computational approaches for the design, development and synthesis of pharmacologically active compounds. For example, methods for automated ligand–receptor docking and techniques for molecular similarity searching have emerged as workhorses for computational and medicinal chemists alike to assist in the task of compound selection for activity screening and hit-to-lead optimization. The Smart application and combination of computational methods and software tools can actually assist in identifying, prioritizing and optimizing novel chemotypes with a desired pharmacological activity profile. While in the early years mere hit retrieval, library shaping and rapid database searching were pursued, more advanced tasks are being aimed at today, such as scaffold-hopping, multidimensional optimization of lead compounds, repurposing of known drugs, de-orphanization of drug targets, prediction of activity profiles and reliable estimations of the free energy of binding, computation of pharmacokinetic and pharmacodynamic drug properties, and simulation of whole biological systems, to name just a few.Many challenges still remain to be successfully met, and a considerable amount of out-of-the-box thinking will be required to solve these problems in computational medicinal chemistry. For example, although it has been realized that ‘similarity’ between two molecules is a context- and target-dependent property, and probably cannot be expressed by a single universally applicable compound representation or description, we still do not fully understand entropic contributions to ligand binding, the dynamic behavior of molecular systems, the role of solvent in molecular complex formation and stability, and the influence of macromolecular assemblies and membranes on drug activity. These, and other essential questions, are addressed in the many valuable contributions in this and the forthcoming thematic issues.Computational medicinal chemistry is an emerging and exciting interdisciplinary field of research. The significant progress and advances made in the recent years are owed to several factors. First, computer technology has dramatically evolved and matured so that algorithms and computational methods can be applied today that could not be used before, owing to hardware limitations. Second, new algorithms and mathematical concepts have been conceived or adapted for specific purposes in medicinal chemistry. Third, numerous successful applications have created increased awareness of computational approaches in the medicinal chemistry community and have also attracted many young scientists and students to the field. Finally, and this statement represents a more personal opinion, there is simply no excuse for not using computational tools for the design of drug candidates. Experimental drug-discovery approaches are ideally supported and complemented by such computer models, which will help us explore new pharmaceutically interesting compounds.In this issueThis issue features a selection of short communications, full research papers and reviews. Willett explores the question of whether similarity searching using 2D fingerprints may not be suitable for scaffold-hopping by reporting results from the evaluation of the effectiveness of six common types of 2D fingerprints after their use for scaffold-hopping similarity searches [2]. Schneider et al. report the use of a combination of complementary virtual-screening tools for the analysis of compounds designed de novo with the aim of inhibiting inactive Polo-like kinase 1, a target for the development of cancer therapeutics [3]. The compounds were analyzed using pharmacophore matching, structure–activity landscape analysis and automated ligand docking, with one compound synthesized and tested in vitro. Bajorath et al. report a large-scale data-mining study whose aim was to identify bioisosteres in publicly available active compounds [4]. They provide a compendium of bioisosteric replacements that are well supported by currently available compound data and which may be of interest for medicinal-chemistry applications. The study by Schmidtke et al. describes the use of shape-focused virtual screening based on a lead compound, katsumadain A, to identify compounds with inhibitory efficacy on influenza neuraminidase and resistance-braking capacity on oseltamivir-resistant strains [5].Mitchell reviews how computational techniques can be adapted and extended to obtain more and higher quality information [6]. Attention is given to specific areas, including the computation of protein–ligand binding affinities, the prediction of off-target bioactivities, bioactivity spectra and computational toxicology. Clark discusses the importance of ‘polar surface area’ to medicinal and computational chemistry and explores approaches for calculating polar surface area using selected examples of relevance to pharmaceutical science [7]. Hutter discusses fingerprint-based similarity approaches, which are computationally fast and allow the comparison of substance databases and, potentially, the identification of new and promising compounds for drug design [8].We invite all readers to enjoy the first instalment in this thematic series.AcknowledgementsWe thank the authors of the articles featuring in the thematic issues for their excellent contributions.Financial & competing interests disclosureGisbert Schneider is a scientific consultant for the pharmaceutical industry in the area of computer-assisted drug design. The author has no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.No writing assistance was utilized in the production of this manuscript.Bibliography1 Kier LB. Book review: neural networks in QSAR and drug design. Devillers J (Ed.), J. Med. Chem.40,2969–2970 (1997).Google Scholar2 Gardiner EJ, Holliday JD, O’Dowd C, Willett P. Effectiveness of 2D fingerprints for scaffold hopping. Fut. Med. Chem.3(4),405–414 (2011).Link, CAS, Google Scholar3 Schneider G, Geppert T, Hartenfeller M et al. Reaction-driven de novo design, synthesis and testing of potential type II kinase inhibitors. Fut. Med. Chem.3(4),415–424 (2011).Link, CAS, Google Scholar4 Wassermann AM, Bajorath J. Large-scale exploration of bioisosteric replacements on the basis of matched molecular pairs. Fut. Med. Chem.3(4),425–435 (2011).Link, CAS, Google Scholar5 Kirchmair J, Rollinger JM, Liedl KR, Seidel N, Krumbholz A & Schmidtke M. Novel neuraminidase inhibitors: identification, biological evaluation and investigations of the binding mode. Fut. Med. Chem.3(4),437–450 (2011).Link, CAS, Google Scholar6 Mitchell JBO. Informatics, machine learning and computational medicinal chemistry. Fut. Med. Chem.3(4),451–467 (2011).Link, CAS, Google Scholar7 Clark DE. What has polar surface area ever done for drug discovery? Fut. Med. Chem.3(4),469–484 (2011).Link, CAS, Google Scholar8 Hutter MC. Graph-based similarity concepts in virtual screening. Fut. Med. Chem.3(4),485–501 (2011).Link, CAS, Google ScholarFiguresReferencesRelatedDetailsCited BySynthesis, Pharmacological, and Biological Evaluation of 2-Furoyl-Based MIF-1 Peptidomimetics and the Development of a General-Purpose Model for Allosteric Modulators (ALLOPTML)21 December 2020 | ACS Chemical Neuroscience, Vol. 12, No. 1Imidazole derivatives as angiotensin II AT1 receptor blockers: Benchmarks, drug-like calculations and quantitative structure-activity relationships modelingChemical Physics Letters, Vol. 696The integration of computational chemistry during drug discovery to drive decisions: are we there yet?Prashant V Desai1 September 2016 | Future Medicinal Chemistry, Vol. 8, No. 14Pushing the boundaries of computational approaches: special focus issue on computational chemistry and computer-aided drug discoveryJürgen Bajorath11 December 2015 | Future Medicinal Chemistry, Vol. 7, No. 18Peptide Scaffolds: Flexible Molecular Structures With Diverse Therapeutic Potentials10 January 2012 | International Journal of Peptide Research and Therapeutics, Vol. 18, No. 2Subsystem quantum mechanics and in silico medicinal and biological chemistryChérif F Matta & Lou Massa18 November 2011 | Future Medicinal Chemistry, Vol. 3, No. 16Computational medicinal chemistry: part IIIGino D’Oca27 June 2011 | Future Medicinal Chemistry, Vol. 3, No. 8Computational medicinal chemistry: part IIGino D’Oca9 May 2011 | Future Medicinal Chemistry, Vol. 3, No. 6 Vol. 3, No. 4 Follow us on social media for the latest updates Metrics History Published online 31 March 2011 Published in print March 2011 Information© Future Science LtdAcknowledgementsWe thank the authors of the articles featuring in the thematic issues for their excellent contributions.Financial & competing interests disclosureGisbert Schneider is a scientific consultant for the pharmaceutical industry in the area of computer-assisted drug design. The author has no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.No writing assistance was utilized in the production of this manuscript.PDF download