oa Editorial [Hot topic: Current Computational Approaches in Medicinal Chemistry (Guest Editor: Santosh A. Khedkar)]

The quote from Charles Dickens's novel is most appropriate in the drug discovery and development field. Concepts are constantly evolving in response to changing economic opportunities and technological advancements. Traditional medicinal chemistry paradigms, relying initially on ‘wet’ chemistry followed by screening and lead optimizations, are expensive and time consuming. On the other hand, initial in silico screening that guides the synthesis and screening of selected compounds has proven to be a better approach to accelerate drug discovery and reduce the cost of the discovery phase. The past 10 years have seen unprecedented scientific advances, with sequencing of the various genomes coupled with advances in proteomics, bioinformatics and cheminformatics, all of which have provided a wealth of information to help speed up drug discovery research and bring newer and better therapies to the market place. The aim of this special issue is to give an overview of and highlight the latest achievements in various computational approaches at a point in time when the field is experiencing tremendous algorithmic advancements in terms of speed and accuracy, with a constant enthusiasm and excitement to meet up the experiments. In this context, virtual screening of small-molecule libraries has attracted academic and industry researchers' attention as a central lead identification and scaffold hopping tool. Amaro et al. elaborate recent advances and future prospects of methods in virtual screening, specifically highlighting issues with protein flexibility and more rigorous estimates of free energies with an aim to rank order the hits and increase enrichment. Loving et al. review various fragment-based discovery and de novo design protocols with cases of successful applications in real-world drug discovery projects. They also exemplify the strengths and weaknesses of these approaches and discuss how one method can be used to complement another and the experiment. The fundamentals of molecular interactions are well understood, but there are challenges to implementing good physical models for hundreds of thousands of possible ligands. Indeed, an accurate calculation of absolute binding affinity in screens of large, diverse libraries is beyond the scope of present tools, but the rank ordering of affinities of disparate hits should be a working compromise. Consequently, Zhau et al. and Menikarachchi et al. discuss the roles of QM and hybrid QM/MM methods, respectively and emphasize their applications to medicinal chemistry problems. Water plays multiple roles in the life of organisms, as a moderator and mediator in protein-ligand interactions. De Beer et al. discuss the influence of water on the ligand-receptor binding process and evaluate the methods of modeling this influence in computational drug design. Further, they review the methods to predict the presence of displaceable waters in protein-ligand complexes, and critically discuss methods of including water in computational drug research. Hydrophobicity impacts every aspect of drug design and even delivery, and determines one of the Lipinski's rule-of-five parameters. Sarkar et al. give a thought-provoking historical perspective on hydrophobicity research, HINT forcefield and its applications in the protein folding and drug design. The Protein Data Bank (PDB) is growing at an ever-increasing rate, but bridging the exponentially increasing gap between the number of protein sequences and their experimental 3D structures will remain beyond its reach. Protein modeling methods try to bridge this gap and have proved efficient in the past in drug design projects. Daga et al. bring together different approaches currently being utilized for 3D structure generation with their advantages (accuracy versus speed), limitations and pitfalls. With its existence over half a century, QSAR still remains one of the prominent computational techniques in structureactivity analysis and screening, thereby expanding its horizons to both ligand-based and structure-based scenarios. QSAR has been the single most-used computational technique and is growing further with newer algorithms and strategies. Verma et al. assess various (nD) QSAR techniques and critically argue the problems associated with alignment-dependency, conformational sensitivity, and choice of statistical methods, biological readout and validation strategies. The pharmaceutical properties of drug candidates determine how much of the drug safely reaches the therapeutic target and dictate its future success as a drug. This so called ADME/T profiling helps guide researchers to optimize the pharmacokinetic and pharmacodynamic properities of lead candidates under development. Prashant Kharkar throws light on newer ADMET prediction algorithms and recent developments to accurately predict these properties. He also traces the underlying obstacles in developing models with better predictivity, with local and/or global applicability and possibility of replacing the in vitro ADME/T testing with in silico screens in the not so distant future. Lastly, Talele et al. make an attempt to highlight the performance of computer-aided drug design field over the years by evaluating the role of computational modeling in identifying and/or optimizing the lead candidates that have entered or successfully outshined the clinical phases of evaluation. Finally, I would like to take this opportunity to thank all the contributing authors of this issue for their valuable time and effort to help this compilation. My special thanks to all the experts who evaluated the article to improve the quality and readability in a timely manner. I extend my special appreciation to Dr. Allen Reitz, editor-in-chief of Current Topics in Medicinal Chemistry, for providing me with this exciting opportunity, and to the editorial and publication team of Bentham Science for their cooperation during the entire process of bringing this issue to the readers. I also thank readers and welcome feedback that will eventually help improve our current state of knowledge and understanding of the field.