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Chagas’ disease, or American trypanosomosiasis, has been the most relevant illness produced by protozoa in Latin America. Synthetic medicinal chemistry efforts have provided an extensive number of chemodiverse hits at the “active-to-hit” stage. However, only a more limited number of these have been studied in vivo in models of Chagas’ disease. Herein, we survey some of the cantidates able to surpass the “hit-to-lead” stage discussing their limitations or merit to enter in clinical trials in the short term.

This chapter analyzes from a pharmaceutical company (pharma) perspective the reasons as to why natural products have fallen out of favor in drug discovery. The current state of drug discovery is first presented, followed by a discussion on the intrinsic utility of natural products in biomedical research, analyzing why, despite so many advantages, it is so difficult for mainstream drug discovery to interface with natural products research. Several strategies to improve natural products drug discovery and make it more efficient and attractive for a pharma side are finally highlighted.

Drug likeness analysis is widely used in modern drug design. However, most drug likeness filters, represented by Lipinski's "Rule of 5", are based on drugs' simple structural features and some physiochemical properties. In this study, we conducted thorough structural analyses for two drug datasets. The first dataset, ADDS, is composed of 1240 FDA-approved drugs, and the second drug dataset, EDDS, is a nonredundant collection of FDA-approved drugs and experimental drugs in different phases of clinical trials from several drug databases (6932 entries). For each molecule, all possible fragments were enumerated using a brutal force approach. Three kinds of building blocks, namely, the drug scaffold, ring system, and the small fragment, were identified and ranked according to the frequencies of their occurrence in drug molecules. The major finding is that most top fragments are essentially common for both drug datasets; the top 50 fragments cover 52.6% and 48.6% drugs for ADDS and EDDS, respectively. The identified building blocks were further ranked according to their relative hit rates in the drug datasets and in a screening dataset, which is a nonredundant collection of screening compounds from many resources. In comparison with the previous reports in the field, we have identified many more high-quality building blocks. The results obtained in this study could provide useful hints to medicinal chemists in designing drug-like compounds as well as prioritizing screening libraries to filter out those molecules lack of functional building blocks.

Drug design has become inconceivable without the assistance of computer-aided methods. In this context in silico was chosen as designation to emphasize the relationship to in vitro and in vivo testing. Nowadays, virtual screening covers much more than estimation of solubility and oral bioavailability of compounds. Along with the challenge of parsing virtual compound libraries, the necessity to model more specific metabolic and toxicological aspects has emerged. Here, recent developments in prediction models are summarized, covering optimization problems in the fields of cytochrome P450 metabolism, blood-brain-barrier permeability, central nervous system activity, and blockade of the hERG-potassium channel. Aspects arising from the use of homology models and quantum chemical calculations are considered with respect to the biological functions. Furthermore, approaches to distinguish drug-like substances from nondrugs by the means of machine learning algorithms are compared in order to derive guidelines for the design of new agents with appropriate properties.

Compounds in drug screening-libraries should resemble pharmaceuticals. To operationally test this, we analysed the compounds in terms of known drug-like filters and developed a novel machine learning method to discriminate approved pharmaceuticals from "drug-like" compounds. This method uses both structural features and molecular properties for discrimination. The method has an estimated accuracy of 91% in discriminating between the Maybridge HitFinder library and approved pharmaceuticals, and 99% between the NATDiverse collection (from Analyticon Discovery) and approved pharmaceuticals. These results show that Lipinski's Rule of 5 for oral absorption is not sufficient to describe "drug-likeness" and be the main basis of screening-library design.

Publisher Summary Several major changes in drug discovery paradigms have occurred, driven both by the pressure to increase the productivity of pharmaceutical research and by expanding knowledge of the underlying biology of diseases. The genomics “revolution” of the early 2000s focused biologists' efforts on developing high-throughput methods for the identification of disease-relevant targets but did not change chemists' two-step paradigm. Moreover, further analysis of the industry record of converting leads to drugs has led to the definition of a concept of “lead-likeness” which has helped better rationalize lead selection criteria and develop lead discovery approaches other than HTS, such as fragment-based screening. Lead-likeness is a tactical guide for selecting starting points for chemical optimization that offers the best chance to deliver “drug-like” candidates at the end of drug discovery programs. Medicinal chemists can chose an infinite number of paths in the chemistry space in their journey from an idea to a drug. Both drug-likeness and lead-likeness concepts steer chemists toward those paths that, based on industry experience, have higher probability of success.

The concept of drug-likeness helps to optimise pharmacokinetic and pharmaceutical properties, for example, solubility, chemical stability, bioavailability and distribution profile. A number of molecular descriptors have emerged as reasonably informative and predictive, for example, the Rule-of-Five. Here, we review some current approaches, then discuss their major shortcoming, namely the static nature of the structural features and physicochemical properties they encode. As we demonstrate, molecules are not 'frozen statues' but 'dancing ballerinas', and several of their computable physicochemical properties are conformation-dependent and lead to the concept of property spaces. Molecular sensitivity (namely, how much a given computable physicochemical property varies as a function of flexibility) appears as a promising descriptor to encode some of the information contained in molecular property spaces.

Kasi Murugan,1 Shanmugasamy Sangeetha,2 Shanmugasamy Ranjitha,2 Antony Vimala,2 Saleh Al-Sohaibani,1 Gopal Rameshkumar21Department of Botany and Microbiology, College of Science, King Saud University, Riyadh, Saudi Arabia; 2Bioinformatics Laboratory, Anna University K. Balachander Research Centre, MIT Campus of Anna University Chennai, Chennai, IndiaAbstract: An histone deacetylase (HDAC) inhibitor database (HDACiDB) was constructed to enable rapid access to data relevant to the development of epigenetic modulators (HDAC inhibitors [HDACi]), helping bring precision cancer medicine a step closer. Thousands of HDACi targeting HDACs are in various stages of development and are being tested in clinical trials as monotherapy and in combination with other cancer agents. Despite the abundance of HDACi, information resources are limited. Tools for in silico experiments on specific HDACi prediction, for designing and analyzing the generated data, as well as custom-made specific tools and interactive databases, are needed. We have developed an HDACiDB that is a composite collection of HDACi and currently comprises 1,445 chemical compounds, including 419 natural and 1,026 synthetic ones having the potential to inhibit histone deacetylation. Most importantly, it will allow application of Lipinski’s rule of five drug-likeness and other physicochemical property-based screening of the inhibitors. It also provides easy access to information on their source of origin, molecular properties, drug likeness, as well as bioavailability with relevant references cited. Being the first comprehensive database on HDACi that contains all known natural and synthetic HDACi, the HDACiDB may help to improve our knowledge concerning the mechanisms of actions of available HDACi and enable us to selectively target individual HDAC isoforms and establish a new paradigm for intelligent epigenetic cancer drug design. Thedatabase is freely available on the http://hdacidb.bioinfo.au-kbc.org.in/hdacidb/ website.Keywords: cancer, drug likeness, histone deacetylase inhibitors, epigenetics, Lipinski’s rule, molecular properties

A scoring scheme for the classification of moleculesinto drugs and non-drugs was established. It was setup by using atom type descriptors for encoding themolecular structures and by training a feed-forward neural network for classifying the molecules. The approach was parameterized by using large databases of drugs and non-drugs - the Available Chemicals Directory (ACD) with 169 331 molecules and the World Drug Index (WDI) with 38 416 molecules. It was able to reveal features in the molecular descriptors that either qualify or disqualify a molecule for being a drug. The method classified about 80% of the ACD and the WDI correctly. It was extended to the application for crop protection compounds and can be used to prioritize compounds for synthesis, purchase, or biological testing. An enhancement allows to optimize the drug character of combinatorial libraries.

Support Vector Machines (SVM) is a powerful classification and regression tool that is becoming increasingly popular in various machine learning applications. We tested the ability of SVM, in comparison with well-known neural network techniques, to predict drug-likeness and agrochemical-likeness for large compound collections. For both kinds of data, SVM outperforms various neural networks using the same set of descriptors. We also used SVM for estimating the activity of Carbonic Anhydrase II (CA II) enzyme inhibitors and found that the prediction quality of our SVM model is better than that reported earlier for conventional QSAR. Model characteristics and data set features were studied in detail.

The concept of drug-likeness, an important characteristic for any compound in a screening library, is nevertheless difficult to pin down. Based on our belief that this concept is implicit within the collective experience of working chemists, we devised a data set to capture an intuitive human understanding of both this characteristic and ease of synthesis, a second key characteristic. Five chemists assigned a pair of scores to each of 3980 diverse compounds, with the component scores of each pair corresponding to drug-likeness and ease of synthesis, respectively. Using this data set, we devised binary classifiers with an artificial neural network and a support vector machine. These models were found to efficiently eliminate compounds that are not drug-like and/or hard-to-synthesize derivatives, demonstrating the suitability of these models for use as compound acquisition filters.

The large-scale application of combinatorial chemistry to drug discovery is an endeavor that is now more than ten years old. The growth of chemical libraries together with the influx of novel genomic targets has led to a reconstruction of the drug-screening paradigm. The drug discovery industry faces a post-genomic world where the interplay between tens-of-thousands of proteins must be addressed. To compound this complexity, there now exists the ability to screen millions of compounds against a single target. This review focuses on the practice and use of selecting individual compounds from large chemical libraries that act on targets relevant to signal transduction.

Drug likeness literally means similarity to known drugs. The ability to assess drug likeness is important in the design of libraries made using high-speed chemistry. If library design is driven primarily by the ease-of synthesis and structural diversity of an array of compounds, it is likely that significant portions of the library will have undesirable properties, for example, high molecular weight or low solubility. For libraries containing many thousands of compounds, computational methods for assessing drug likeness need to be applied to filter out molecules with potential liabilities before resources are spent on synthesis, characterization, and high-throughput screening. There are three ways by which drug likeness can be assessed: avoiding known threats, mimicking known drugs, and direct property prediction. Medicinal chemists design chemical structures with desirable properties, and then choose appropriate synthetic routes utilizing available synthetic precursors. General libraries for screening aim at high structural diversity, while lead optimization libraries could focus on a specific structural feature. In each case, there are two ways of ensuring the drug-like character of the library. First is to design a library based on diversity considerations, and then to apply any of the “drug likeness” filters described in this chapter. Second is to optimize diversity/similarity, “drug-likeness”, and any other relevant properties simultaneously using an appropriate optimization technique, for example, simulated annealing or genetic algorithms.

An analysis of the origins of recently launched drugs reveals that most were derived by modification of known drug structures or from lead structures obtained from the scientific literature. High-throughput screening did not have a significant impact on the derivation of these drugs. The drug structures are very closely related to their leads.

Recent developments in combinatorial chemistry and high-throughput screening have dramatically increased the scale on which drug discovery programs are carried out. Along with these advances has come a need for automated methods of determining which compounds from a library should be synthesized and screened. These methods range from simple counting schemes to sophisticated machine learning techniques such as neural networks. While many of these methods have performed well in validation studies, the field is still in its formative stage. This paper reviews a number of computational techniques for identifying drug-like molecules and examines challenges facing the field.

Recent advances in high-throughput protein structure determination and in computational chemistry have refocussed attention on virtual screening and fast automated docking methods. This review provides a brief introduction to the basic ideas and outlines computational tools currently used. We also provide several examples of where virtual screening has proved successful, highlighting the usefulness of the approach.

Recently, one of the key trends in the pharmaceutical industry has been the integration of what have traditionally been considered 'development' activities into the early phases of drug discovery. The aim of this paradigm shift is the prompt identification and elimination of candidate molecules that are unlikely to survive later stages of discovery and development. In this review, the authors examine the growing role that is being played by computational methods in this filtering process, with a particular focus on the prediction of intestinal absorption and blood-brain barrier penetration.

A scoring scheme for the rapid and automatic classification of molecules into drugs and nondrugs was developed. The method is a valuable new tool that can aid in the selection and prioritization of compounds from large compound collections for purchase or biological testing and that can replace a considerable amount of laborious manual work by a more unbiased approach. It is based on the extraction of knowledge from large databases of drugs and nondrugs. The method was set up by using atom type descriptors for encoding the molecular structures and by training a feedforward neural network for classifying the molecules. It was parametrized and validated by using large databases of drugs and nondrugs (169 331 molecules from the Available Chemicals Directory, ACD, and 38 416 molecules from the World Drug Index, WDI). The method revealed features in the molecular descriptors that either qualify or disqualify a molecule for being a drug and classified 83% of the ACD and 77% of the WDI adequately.

Background In this work, we analyzed and compared the distribution profiles of a wide variety of molecular properties for three compound classes: drug-like compounds in MDL Drug Data Report (MDDR), non-drug-like compounds in Available Chemical Directory (ACD), and natural compounds in Traditional Chinese Medicine Compound Database (TCMCD). Results The comparison of the property distributions suggests that, when all compounds in MDDR, ACD and TCMCD with molecular weight lower than 600 were used, MDDR and ACD are substantially different while TCMCD is much more similar to MDDR than ACD. However, when the three subsets of ACD, MDDR and TCMCD with similar molecular weight distributions were examined, the distribution profiles of the representative physicochemical properties for MDDR and ACD do not differ significantly anymore, suggesting that after the dependence of molecular weight is removed drug-like and non-drug-like molecules cannot be effectively distinguished by simple property-based filters; however, the distribution profiles of several physicochemical properties for TCMCD are obviously different from those for MDDR and ACD. Then, the performance of each molecular property on predicting drug-likeness was evaluated. No single molecular property shows good performance to discriminate between drug-like and non-drug-like molecules. Compared with the other descriptors, fractional negative accessible surface area (FASA-) performs the best. Finally, a PCA-based scheme was used to visually characterize the spatial distributions of the three classes of compounds with similar molecular weight distributions. Conclusion If FASA- was used as a drug-likeness filter, more than 80% molecules in TCMCD were predicted to be drug-like. Moreover, the principal component plots show that natural compounds in TCMCD have different and even more diverse distributions than either drug-like compounds in MDDR or non-drug-like compounds in ACD.

Aim: The aim of our study was to develop new ibuprofen, that is better effect and less side effect using computer aided drug design. We found totally 10 molecular modifications of ibuprofen. Accordingly structure 9 is considered as the most appropriately modified ibuprofen drug that would have significant effect than the parent ibuprofen. Objective: To study Insilico molecular modifications of ibuprofen with physiochemical properties and drug likeness parameters to develop better drug with less side effect. Materials and Method: Molinspiration and Chemsketch are the two types of softwares used to perform the molecular modelings. Result: The Structure 9 is a best candidate among 10 structures, because it is similar to acelofenac, derivative of diclofenac which is already available in the market. Detailed molecular modeling using Dockin studies and toxicities studies can be carried out before synthesis of these derivatives.

The low success rate of converting lead compounds into drugs owing to unfavorable pharmacokinetic parameters has evoked a renewed interest in understanding more clearly what makes a compound drug-like. This article reviews a number of computational techniques for identifying drug-like molecules, ranging from simple counting schemes to sophisticated machine learning techniques such as neural networks, along with their application and challenges.

We exploit the recent availability of a community reconstruction of the human metabolic network (‘Recon2’) to study how close in structural terms are marketed drugs to the nearest known metabolite(s) that Recon2 contains. While other encodings using different kinds of chemical fingerprints give greater differences, we find using the 166 Public MDL Molecular Access (MACCS) keys that 90 % of marketed drugs have a Tanimoto similarity of more than 0.5 to the (structurally) ‘nearest’ human metabolite. This suggests a ‘rule of 0.5’ mnemonic for assessing the metabolite-like properties that characterise successful, marketed drugs. Multiobjective clustering leads to a similar conclusion, while artificial (synthetic) structures are seen to be less human-metabolite-like. This ‘rule of 0.5’ may have considerable predictive value in chemical biology and drug discovery, and may represent a powerful filter for decision making processes. Electronic supplementary material The online version of this article (doi:10.1007/s11306-014-0733-z) contains supplementary material, which is available to authorized users.