The process of structure-based drug design

The field of structure-based drug design is a rapidly growing area in which many successes have occurred in recent years. The explosion of genomic, proteomic, and structural information has provided hundreds of new targets and opportunities for future drug lead discovery. This review summarizes the process of structure-based drug design and includes, primarily, the choice of a target, the evaluation of a structure of that target, the pivotal questions to consider in choosing a method for drug lead discovery, and evaluation of the drug leads. Key principles in the field of structure-based drug design will be illustrated through a case study that explores drug design for AmpC beta-lactamase.     

Performance evaluation of 2D fingerprint and 3D shape similarity methods in virtual screening

Virtual screening (VS) can be accomplished in either ligand- or structure-based methods. In recent times, an increasing number of 2D fingerprint and 3D shape similarity methods have been used in ligand-based VS. To evaluate the performance of these ligand-based methods, retrospective VS was performed on a tailored directory of useful decoys (DUD). The VS performances of 14 2D fingerprints and four 3D shape similarity methods were compared. The results revealed that 2D fingerprints ECFP_2 and FCFP_4 yielded better performance than the 3D Phase Shape methods. These ligand-based methods were also compared with structure-based methods, such as Glide docking and Prime molecular mechanics generalized Born surface area rescoring, which demonstrated that both 2D fingerprint and 3D shape similarity methods could yield higher enrichment during early retrieval of active compounds. The results demonstrated the superiority of ligand-based methods over the docking-based screening in terms of both speed and hit enrichment. Therefore, considering ligand-based methods first in any VS workflow would be a wise option.     

Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process

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 structure–activity 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.     

Chemistry in Times of Artificial Intelligence

Chemists have to a large extent gained their knowledge by doing experiments and thus gather data. By putting various data together and then analyzing them, chemists have fostered their understanding of chemistry. Since the 1960s, computer methods have been developed to perform this process from data to information to knowledge. Simultaneously, methods were developed for assisting chemists in solving their fundamental questions such as the prediction of chemical, physical, or biological properties, the design of organic syntheses, and the elucidation of the structure of molecules. This eventually led to a discipline of its own: chemoinformatics. Chemoinformatics has found important applications in the fields of drug discovery, analytical chemistry, organic chemistry, agrichemical research, food science, regulatory science, material science, and process control. From its inception, chemoinformatics has utilized methods from artificial intelligence, an approach that has recently gained more momentum.     

Free resources to assist structure-based virtual ligand screening experiments

In today's research environment, a wealth of experimental/theoretical structural data is available and the number of therapeutically relevant macromolecular structures is growing rapidly. This, coupled with the huge number of small non-peptide potential drug candidates easily available (over 7 million compounds), highlight the need of using computer-aided techniques for the efficient identification and optimization of novel hit compounds. Virtual (or in silico) ligand screening based on the three-dimensional structure of macromolecular targets (SB-VLS) is firmly established as an important approach to identify chemical entities that have a high likelihood of binding to a target molecule to elicit desired biological responses. A myriad of free applications and services facilitating the drug discovery process have been posted on the Web. In this review, we cite over 350 URLs that are useful for SB-VLS projects and essentially free for academic groups. We attempt to provide links for in silico ADME/tox prediction tools, compound collections, some ligand-based methods, characterization/simulation of 3D targets and homology modeling tools, druggable pocket predictions, active site comparisons, analysis of macromolecular interfaces, protein docking tools to help identify binding pockets and protein-ligand docking/scoring methods. As such, we aim at providing both, methods pertaining to the field of Structural Bioinformatics (defined here as tools to study macromolecules) and methods pertaining to the field of Chemoinformatics (defined here as tools to make better decisions faster in the arena of drug/lead identification and optimization). We also report several recent success stories using these free computer methods. This review should help readers finding free computer tools useful for their projects. Overall, we are confident that these tools will facilitate rapid and cost-effective identification of new hit compounds. The URLs presented in this review will be updated regularly at www.vls3d.com in the coming months, "Links" section.     

Repositioning of Quinazolinedione-Based Compounds on Soluble Epoxide Hydrolase (sEH) through 3D Structure-Based Pharmacophore Model-Driven Investigation

The development of new bioactive compounds represents one of the main purposes of the drug discovery process. Various tools can be employed to identify new drug candidates against pharmacologically relevant biological targets, and the search for new approaches and methodologies often represents a critical issue. In this context, in silico drug repositioning procedures are required even more in order to re-evaluate compounds that already showed poor biological results against a specific biological target. 3D structure-based pharmacophoric models, usually built for specific targets to accelerate the identification of new promising compounds, can be employed for drug repositioning campaigns as well. In this work, an in-house library of 190 synthesized compounds was re-evaluated using a 3D structure-based pharmacophoric model developed on soluble epoxide hydrolase (sEH). Among the analyzed compounds, a small set of quinazolinedione-based molecules, originally selected from a virtual combinatorial library and showing poor results when preliminarily investigated against heat shock protein 90 (Hsp90), was successfully repositioned against sEH, accounting the related built 3D structure-based pharmacophoric model. The promising results here obtained highlight the reliability of this computational workflow for accelerating the drug discovery/repositioning processes.     

Recent Advances in In Silico Target Fishing

In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.     

Bioluminescence and chemiluminescence in drug screening

Drug screening, that is, the evaluation of the biological activity of candidate drug molecules, is a key step in the drug discovery and development process. In recent years, high-throughput screening assays have become indispensable for early stage drug discovery because of the developments in synthesis technologies, such as combinatorial chemistry and automated synthesis, and the discovery of an increasing number of new pharmacological targets.Bioluminescence and chemiluminescence represent suitable detection techniques for high-throughput screening because they allow rapid and sensitive detection of the analytes and can be applied to small-volume samples. In this paper we report on recent applications of bioluminescence and chemiluminescence in drug screening, both for in vitro and in vivo assays. Particular attention is devoted to the latest and most innovative bioluminescence and chemiluminescence-based technologies for drug screening, such as assays based on genetically modified cells, bioluminescence resonance energy transfer (BRET)-based assays, and in vivo imaging assays using transgenic animals or bioluminescent markers. The possible relevance of bioluminescence and chemiluminescence techniques in the future developments of high-throughput screening technologies is also discussed.     

Lessons learned in induced fit docking and metadynamics in the Drug Design Data Resource Grand Challenge 2

Two of the major ongoing challenges in computational drug discovery are predicting the binding pose and affinity of a compound to a protein. The Drug Design Data Resource Grand Challenge 2 was developed to address these problems and to drive development of new methods. The challenge provided the 2D structures of compounds for which the organizers help blinded data in the form of 35 X-ray crystal structures and 102 binding affinity measurements and challenged participants to predict the binding pose and affinity of the compounds. We tested a number of pose prediction methods as part of the challenge; we found that docking methods that incorporate protein flexibility (Induced Fit Docking) outperformed methods that treated the protein as rigid. We also found that using binding pose metadynamics, a molecular dynamics based method, to score docked poses provided the best predictions of our methods with an average RMSD of 2.01 Å. We tested both structure-based (e.g. docking) and ligand-based methods (e.g. QSAR) in the affinity prediction portion of the competition. We found that our structure-based methods based on docking with Smina (Spearman ρ?=?0.614), performed slightly better than our ligand-based methods (ρ?=?0.543), and had equivalent performance with the other top methods in the competition. Despite the overall good performance of our methods in comparison to other participants in the challenge, there exists significant room for improvement especially in cases such as these where protein flexibility plays such a large role.     

In silico prediction of cytochrome P450-mediated drug metabolism

The application of combinatorial chemistry and high-throughput screening technique enables the large number of chemicals to be generated and tested simultaneously, which will facilitate the drug development and discovery. At the same time, it brings about a challenge of how to efficiently identify the potential drug candidates from thousands of compounds. A way used to deal with the challenge is to consider the drug pharmacokinetic properties, such as absorption, distribution, metabolism and excretion (ADME), in the early stage of drug development. Among ADME properties, metabolism is of importance due to the strong association with efficacy and safety of drug. The review will focus on in silico approaches for prediction of Cytochrome P450-mediated drug metabolism. We will describe these predictive methods from two aspects, structure-based and data-based. Moreover, the applications and limitations of various methods will be discussed. Finally, we provide further direction toward improving the predictive accuracy of these in silico methods.     

Capillary electrophoretic methods in the development of metal-based therapeutics and diagnostics: new methodology and applications

In recent years, capillary electrophoresis (CE) has matured to a standard method in medicinal inorganic chemistry. More and more steps of the drug discovery process are followed by CE. However, not only the number of applications has steadily increased but also the variety of used methodology has significantly broadened and, as compared to a few years ago, a wider scope of separation modes and hyphenated systems has been used. Herein, a summary of the newly utilized CE methods and their applications in metallodrug research in the timeframe 2006-2011 is presented, following related reviews from 2003 and 2007 (Electrophoresis, 2003, 24, 2023-2037; Electrophoresis 2007, 28, 3436-3446). Areas covered include impurity profiling, quality control of pharmaceutical formulations, lipophilicity estimation, interactions between metallodrugs and proteins or nucleotides, and characterization and also quantification of metabolites in biological matrices and real-world samples.     

Unmodified methodologies in target discovery for small molecule drugs: A rising star

Target discovery, involving target identification and validation, is the prerequisite for drug discovery and screening. Novel methodologies and technologies for the precise discovery and confirmation of drug targets are powerful tools in understanding the disease, looking for a drug and elucidating the mechanism of drug treatment. Among the common target identification and confirmation methods, the modified method is time-consuming and laborious, which may reduce or change the activity of natural products. The unmodified methods developed in recent years without chemical modification have gradually become an important means of studying drug targets. A wide range of unmodified approaches have been reported, introducing and analyzing the recent emerging methodologies and technologies. This review highlights the advantages and limitations of these methods for the application of drug target discovery and presents an overview of their contributions to the target discovery of small molecule drugs. The application and future development trends of methodologies in target discovery are also prospected to provide a reference for drug target research.     

Applications of Activity‐Based Protein Profiling (ABPP) and Bioimaging in Drug Discovery

Activity‐based protein profiling (ABPP) and bioimaging have been developed in recent years as powerful technologies in drug discovery. Specifically, both approaches can be applied in critical steps of drug development, such as therapy target discovery, high‐throughput drug screening and target identification of bioactive molecules. We have been focused on the development of various strategies that enable simultaneous activity‐based protein profiling and bioimaging studies, thus facilitating an understanding of drug actions and potential toxicities. In this Minireview, we summarize these novel strategies and applications, with the aim of promoting these technologies in drug discovery.     

Exploring polypharmacology using a ROCS-based target fishing approach

Polypharmacology has emerged as a new theme in drug discovery. In this paper, we studied polypharmacology using a ligand-based target fishing (LBTF) protocol. To implement the protocol, we first generated a chemogenomic database that links individual protein targets with a specified set of drugs or target representatives. Target profiles were then generated for a given query molecule by computing maximal shape/chemistry overlap between the query molecule and the drug sets assigned to each protein target. The overlap was computed using the program ROCS (Rapid Overlay of Chemical Structures). We validated this approach using the Directory of Useful Decoys (DUD). DUD contains 2950 active compounds, each with 36 property-matched decoys, against 40 protein targets. We chose a set of known drugs to represent each DUD target, and we carried out ligand-based virtual screens using data sets of DUD actives seeded into DUD decoys for each target. We computed Receiver Operator Characteristic (ROC) curves and associated area under the curve (AUC) values. For the majority of targets studied, the AUC values were significantly better than for the case of a random selection of compounds. In a second test, the method successfully identified off-targets for drugs such as rimantadine, propranolol, and domperidone that were consistent with those identified by recent experiments. The results from our ROCS-based target fishing approach are promising and have potential application in drug repurposing for single and multiple targets, identifying targets for orphan compounds, and adverse effect prediction.     

New techniques and strategies in drug discovery

Great success has been witnessed in last decades, some new techniques and strategies have been widely used in drug discovery. In this roadmap, several representative techniques and strategies are highlighted to show recent advances in this filed. (A) A DOX protocol has been developed for accurate protein-ligand binding structure prediction, in which first principle method was used to rank the binding poses. Validation against crystal structures have found that DOX prediction achieved an impressive success rate of 99%, indicating significant improvement over molecular docking method. (B) Virtual target profiling is a compound-centric strategy enabling a parallel implementation of interrogating compounds against various targets in a single screen, which has been used in hit/lead identification, drug repositioning, and mechanism-of-action studies. Current and emerging methods for virtual target profiling are briefly summarized herein. (C) Research on targeted autophagy to treat diseases has received encouraging progress. However, due to the complexity of autophagy and disease, experimental and in silico methods should be performed synergistically for the entire process. This part focuses on in silico methods in autophagy research to promote their use in medicinal research. (D) Histone deacetylases (HDACs) play important roles in various biological functions through the deacetylation of lysine residues. Recent studies demonstrated that HDACs, which possess low deacetylase activities, exhibited more efficient defatty-acylase activities. Here, we review the defatty-acylase activity of HDACs and describe examples for the design of isoform selective HDAC inhibitor. (E) The FDA approval of three kinase allosteric inhibitors and some others entering clinical study has spurred considerable interests in this targeted drug discovery area. (F) Recent advances are reviewed in structure-based design of novel antiviral agents to combat drug resistance. (G) Since nitric oxide (NO) exerts anticancer activity depending on its concentration, optimal levels of NO in cancer cells is desirable. In this minireview, we briefly describe recent advances in the research of NO-based anticancer agents by our group and present some opinions on the future development of these agents. (H) The field of photoactivation strategies have been extensively developed for controlling chemical and biological processes with light. This review will summarize and provide insight into recent research advances in the understanding of photoactivatable molecules including photoactivatable caged prodrugs and photoswitchable molecules.     

Chemomics and drug innovation

Chemomics is an interdisciplinary study using approaches from chemoinformatics,bioinformatics,synthetic chemistry,and other related disciplines.Biological systems make natural products from endogenous small molecules (natural product building blocks) through a sequence of enzyme catalytic reactions.For each reaction,the natural product building blocks may contribute a group of atoms to the target natural product.We describe this group of atoms as a chemoyl.A chemome is the complete set of chemoyls in an organism.Chemomics studies chemomes and the principles of natural product syntheses and evolutions.Driven by survival and reproductive demands,biological systems have developed effective protocols to synthesize natural products in order to respond to environmental changes;this results in biological and chemical diversity.In recent years,it has been realized that one of the bottlenecks in drug discovery is the lack of chemical resources for drug screening.Chemomics may solve this problem by revealing the rules governing the creation of chemical diversity in biological systems,and by developing biomimetic synthesis approaches to make quasi natural product libraries for drug screening.This treatise introduces chemomics and outlines its contents and potential applications in the fields of drug innovation.     

化学基元组学与药物创新

化学基元组学（chemomics）是与化学信息学、生物信息学、合成化学等学科相关的交叉学科．生物系统从内源性小分子（天然砌块）出发,通过酶催化的化学反应序列制造天然产物．生物系统通过化学反应和天然砌块向目标天然产物“砌入”一组原子,这样的一组原子称为化学基元（chemoyl）．化学基元组（chemome）是生物组织中所含有的化学基元的全体．化学基元组学研究各种化学基元的结构、组装与演化的基本规律．在生存压力和繁衍需求的驱动下,生物系统已经进化出有效手段来合成天然产物以应付环境的变化,并产生了丰富多彩的生物和化学多样性．近年来,人们意识到药物创新的瓶颈之一是药物筛选资源的日益枯竭．化学基元组学可以解决这个瓶颈问题,它通过揭示生物系统制备化学多样性的规律,发展仿生合成方法制备类天然化合物库（quasi natural product libraries）以供药物筛选．本文综述了化学基元组学的主要研究内容及其在药物创新各领域中的潜在应用．