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Computer-assisted synthesis planning has become increasingly important in drug discovery. While deep-learning models have shown remarkable progress in achieving high accuracies for single-step retrosynthetic predictions, their performances in retrosynthetic route planning need to be checked. This study compares the intricate single-step models with a straightforward template enumeration approach for retrosynthetic route planning on a real-world drug molecule data set. Despite the superior single-step accuracy of advanced models, the template enumeration method with a heuristic-based retrosynthesis knowledge score was found to surpass them in efficiency in searching the reaction space, achieving a higher or comparable solve rate within the same time frame. This counterintuitive result underscores the importance of efficiency and retrosynthesis knowledge in retrosynthesis route planning and suggests that future research should incorporate a simple template enumeration as a benchmark. It also suggests that this simple yet effective strategy should be considered alongside more complex models to better cater to the practical needs of computer-assisted synthesis planning in drug discovery.

Biologically active metabolites are a valuable resource for development of drug candidates and lead structures for drug design. This digest highlights a selection of biologically active metabolites that have been used as new chemical entities for development or as lead structures for drug design., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Having always been driven by the need to get new treatments to patients as quickly as possible, drug discovery is a constantly evolving process. This chapter will review how medicinal chemistry was established, how it has changed over the years due to the emergence of new enabling technologies, and how early advances in synthesis, purification and analysis, have provided the foundations upon which the current automated and enabling technologies are built. Looking beyond the established technologies, this chapter will also consider technologies that are now emerging, and their impact on the future of drug discovery and the role of medicinal chemists., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Drugs in the chemical space beyond the rule of 5 (bRo5) can modulate targets with difficult binding sites while retaining cell permeability and oral absorption. Reviewing the syntheses of bRo5 drugs approved since 1990 highlights synthetic chemistry's contribution to drug discovery in this space. Initially, bRo5 drugs were mainly natural products and semi-synthetic derivatives. Later, peptidomimetics and de novo designed compounds, that include up to seven chiral centres and macrocyclic rings became dominant. These drugs are prepared by total synthesis, sometimes by routes of more than 25 steps with stereocentres originating from the chiral pool, or being installed by chiral induction or enzymatic resolution. Interestingly, ring-closing metathesis proved to be the method of choice for macrocyclisation in hepatitis C virus protease inhibitors. We conclude that structural simplification, planning of synthetic routes regarding incorporation of stereocentres and macrocyclisation, as well as incorporation of structural knowledge and consideration of chameleonic properties in design, should facilitate drug discovery in bRo5 space., (© 2020 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Simple physico-chemical properties, like logD, solubility, or melting point, can reveal a great deal about how a compound under development might later behave. These data are typically measured for most compounds in drug discovery projects in a medium throughput fashion. Collecting and assembling all the Bayer in-house data related to these properties allowed us to apply powerful machine learning techniques to predict the outcome of those assays for new compounds. In this paper, we report our finding that, especially for predicting physicochemical ADMET endpoints, a multitask graph convolutional approach appears a highly competitive choice. For seven endpoints of interest, we compared the performance of that approach to fully connected neural networks and different single task models. The new model shows increased predictive performance compared to previous modeling methods and will allow early prioritization of compounds even before they are synthesized. In addition, our model follows the generalized solubility equation without being explicitly trained under this constraint.

Introduction : Late-stage functionalization (LSF) can introduce important chemical groups in the very last steps of the synthesis. LSF has the potential to speed up the preparation of novel chemical entities and diverse chemical libraries and have a major impact on drug discovery. Functional group tolerance and mild conditions allows access to new molecules not easily accessible by conventional approaches without the need for laborious de novo chemical synthesis. Areas Covered : A historical overview of late-stage functionalization and its applicability to drug discovery is provided. Pioneering methodologies that laid the foundations for the field are briefly covered and archetypal examples of their application to drug discovery are discussed. Novel methodologies reported in the past few years mainly stemming from the recent renaissances of photoredox catalysis and radical chemistry are reviewed and their application to drug discovery considered. Expert opinion : It is envisioned that late-stage functionalization will improve the efficiency and efficacy of drug discovery. There is evidence of the widespread uptake of LSF by the medicinal chemistry community and it is expected that the recent and continuing endeavors of many academic laboratories and pharmaceutical companies will soon have an impact on drug development.

Innovations in synthetic chemistry have enabled the discovery of many breakthrough therapies that have improved human health over the past century. In the face of increasing challenges in the pharmaceutical sector, continued innovation in chemistry is required to drive the discovery of the next wave of medicines. Novel synthetic methods not only unlock access to previously unattainable chemical matter, but also inspire new concepts as to how we design and build chemical matter. We identify some of the most important recent advances in synthetic chemistry as well as opportunities at the interface with partner disciplines that are poised to transform the practice of drug discovery and development., (Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

The largest family of drug targets in clinical trials constitute of GPCRs (G-protein coupled receptors) which accounts for about 34% of FDA (Food and Drug Administration) approved drugs acting on 108 unique GPCRs. Factors such as readily identifiable conserved motif in structures, 127 orphan GPCRs despite various de-orphaning techniques, directed functional antibodies for validation as drug targets, etc. has widened their therapeutic windows. The availability of 44 crystal structures of unique receptors, unexplored non-olfactory GPCRs (encoded by 50% of the human genome) and 205 ligand receptor complexes now present a strong foundation for structure-based drug discovery and design. The growing impact of polypharmacology for complex diseases like schizophrenia, cancer etc. warrants the need for novel targets and considering the undiscriminating and selectivity of GPCRs, they can fulfill this purpose. Again, natural genetic variations within the human genome sometimes delude the therapeutic expectations of some drugs, resulting in medication response differences and ADRs (adverse drug reactions). Around ~30 billion US dollars are dumped annually for poor accounting of ADRs in the US alone. To curb such undesirable reactions, the knowledge of established and currently in clinical trials GPCRs families can offer huge understanding towards the drug designing prospects including "off-target" effects reducing economical resource and time. The druggability of GPCR protein families and critical roles played by them in complex diseases are explained. Class A, class B1, class C and class F are generally established family and GPCRs in phase I (19%), phase II(29%), phase III(52%) studies are also reviewed. From the phase I studies, frizzled receptors accounted for the highest in trial targets, neuropeptides in phase II and melanocortin in phase III studies. Also, the bioapplications for nanoparticles along with future prospects for both nanomedicine and GPCR drug industry are discussed. Further, the use of computational techniques and methods employed for different target validations are also reviewed along with their future potential for the GPCR based drug discovery., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Background: Discovery and development of a new drug is a long lasting and expensive journey that takes around 20 years from starting idea to approval and marketing of new medication. Despite R&D expenditures have been constantly increasing in the last few years, the number of new drugs introduced into market has been steadily declining. This is mainly due to preclinical and clinical safety issues, which still represent about 40% of drug discontinuation. To cope with this issue, a number of in silico techniques are currently being used for an early stage evaluation/prediction of potential safety issues, allowing to increase the drug-discovery success rate and reduce costs associated with the development of a new drug., Methods: In the present review, we will analyse the early steps of the drug-discovery pipeline, describing the sequence of steps from disease selection to lead optimization and focusing on the most common in silico tools used to assess attrition risks and build a mitigation plan., Results: A comprehensive list of widely used in silico tools, databases, and public initiatives that can be effectively implemented and used in the drug discovery pipeline has been provided. A few examples of how these tools can be problem-solving and how they may increase the success rate of a drug discovery and development program have been also provided. Finally, selected examples where the application of in silico tools had effectively contributed to the development of marketed drugs or clinical candidates will be given., Conclusion: The in silico toolbox finds great application in every step of early drug discovery: (i) target identification and validation; (ii) hit identification; (iii) hit-to-lead; and (iv) lead optimization. Each of these steps has been described in details, providing a useful overview on the role played by in silico tools in the decision-making process to speed-up the discovery of new drugs., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Macrocycles are an emerging and largely underexploited part of chemical space where potential drugs for difficult genomic targets can be discovered. Macrocycles can have advantages over their natural twins such as better control over synthesis, physicochemical properties and target binding. Fast and convergent synthesis pathways are underdeveloped. Multicomponent reaction (MCR) chemistry is very well suited for the synthesis of a diverse range of macrocycles and is also able to generate great levels of molecular diversity and complexity at low synthetic costs., (Copyright © 2018. Published by Elsevier Ltd.)

The pharmaceutical industry stands on the brink of a revolution, calling for the recognition and embracement of novel techniques. 3D printing (3DP) is forecast to reshape the way in which drugs are designed, manufactured, and used. Although a clear trend towards personalised fabrication is perceived, here we accentuate the merits and shortcomings of each technology, providing insights into aspects such as the efficiency of production, global supply, and logistics. Contemporary opportunities for 3DP in drug discovery and pharmaceutical development and manufacturing are unveiled, offering a forward-looking view on its potential uses as a digitised tool for personalised dispensing of drugs., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Simple comparative correlation analyses and quantitative structure-kinetics relationship (QSKR) models highlight the interplay of kinetic rates and binding affinity as an essential feature in drug design and discovery. The choice of the molecular series, and their structural variations, used in QSKR modeling is fundamental to understanding the mechanistic implications of ligand and/or drug-target binding and/or unbinding processes. Here, we discuss the implications of linear correlations between kinetic rates and binding affinity constants and the relevance of the computational approaches to QSKR modeling., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

AI comes to lead optimization: medicinal chemistry in all disease areas can be accelerated by exploiting our pre-competitive knowledge in an unbiased way., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Here, we propose the extension of the quality-by-design (QbD) concept to also fit the early development phases of pharmaceuticals by adding elements that are currently widely applied, but not yet included in the QbD model in a structured way. These are the introduction of a 'zero' preformulation phase (i.e., selection of drug substance, possible dosage forms and administration routes based on the evaluated therapeutic need); building in stakeholders' (industry, patient, and regulatory) requirements into the quality target product profile (QTTP); and the use of modern quality management tools during the composition and process design phase [collecting critical quality attributes (CQAs) and selection of CPPs) for (still laboratory-scale) design space (DS) development. Moreover, during industrial scale-up, CQAs (as well as critical process parameters; CPPs) can be changed; however, we recommend that the existing QbD elements are reconsidered and updated after this phase., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Late-stage functionalization (LSF) is a powerful method to quickly generate new analogues of a lead structure without resorting to de novo synthesis. We have leveraged Baran Diversinates to carry out late-stage functionalizations on lead structures from internal drug discovery projects and accurately predicted regioselectivities using computational methods. Our functionalization successfully afforded specific regioisomers which were in line with our predictions. To enhance reactivity, decrease reaction time, and increase reaction yields, we have developed new functionalization conditions involving iron(III) catalysis. Finally, we demonstrate how our LSF reactions using Baran Diversinates can lead to new analogues with improved in vitro DMPK parameters., (© 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

3D printing (3DP) is forecast to be a highly revolutionary technology within the pharmaceutical sector. In particular, the main benefits of 3DP lie in the production of small batches of medicines, each with tailored dosages, shapes, sizes and release characteristics. The manufacture of medicines in this way may finally lead to the concept of personalised medicines becoming a reality. In the shorter term, 3DP could be extended throughout the drug development process, ranging from preclinical development and clinical trials, through to frontline medical care. In this review, we provide a timely perspective on the motivations and potential applications of 3DP pharmaceuticals, as well as a practical viewpoint on how 3DP could be integrated across the pharmaceutical space., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Despite decades of ground-breaking research in academia, organic synthesis is still a rate-limiting factor in drug-discovery projects. Here we present some current challenges in synthetic organic chemistry from the perspective of the pharmaceutical industry and highlight problematic steps that, if overcome, would find extensive application in the discovery of transformational medicines. Significant synthesis challenges arise from the fact that drug molecules typically contain amines and N-heterocycles, as well as unprotected polar groups. There is also a need for new reactions that enable non-traditional disconnections, more C-H bond activation and late-stage functionalization, as well as stereoselectively substituted aliphatic heterocyclic ring synthesis, C-X or C-C bond formation. We also emphasize that syntheses compatible with biomacromolecules will find increasing use, while new technologies such as machine-assisted approaches and artificial intelligence for synthesis planning have the potential to dramatically accelerate the drug-discovery process. We believe that increasing collaboration between academic and industrial chemists is crucial to address the challenges outlined here.

Introduction: PPIs are involved in every disease and specific modulation of these PPIs with small molecules would significantly improve our prospects of developing therapeutic agents. Both industry and academia have engaged in the identification and use of PPI inhibitors. However in comparison, the opposite strategy of employing small-molecule stabilizers of PPIs is underrepresented in drug discovery. Areas covered: PPI stabilization has not been exploited in a systematic manner. Rather, this concept validated by a number of therapeutically used natural products like rapamycin and paclitaxel has been shown retrospectively to be the basis of the activity of synthetic molecules originating from drug discovery projects among them lenalidomide and tafamidis. Here, the authors cover the growing number of synthetic small-molecule PPI stabilizers to advocate for a stronger consideration of this as a drug discovery approach. Expert opinion: Both the natural products and the growing number of synthetic molecules show that PPI stabilization is a viable strategy for drug discovery. There is certainly a significant challenge to adapt compound libraries, screening techniques and downstream methodologies to identify, characterize and optimize PPI stabilizers, but the examples of molecules reviewed here in our opinion justify these efforts.

New drugs introduced to the market every year represent privileged structures for particular biological targets. These new chemical entities (NCEs) provide insight into molecular recognition while serving as leads for designing future new drugs. This annual review describes the most likely process-scale synthetic approaches to 29 new chemical entities (NCEs) that were approved for the first time in 2015.

With the development of ever-expanding synthetic methodologies, a medicinal chemist's toolkit continues to swell. However, with finite time and resources as well as a growing understanding of our field's environment impact, it is critical to refine what can be made to what should be made. This review seeks to highlight multiple cheminformatic approaches in drug discovery that can influence and triage design and execution impacting the likelihood of rapidly generating high-value molecules in a more sustainable manner. This strategy gives chemists the tools to design and refine vast libraries, stress "druglikeness", and rapidly identify SAR trends. Project success, i.e., identification of a clinical candidate, is then reached faster with fewer molecules with the farther-reaching ramification of using fewer resources and generating less waste, thereby helping "green" our field.

There is a pressing need for new technologies that improve the efficacy and efficiency of drug discovery. Structure-based methods have contributed towards this goal but they focus on predicting the binding affinity of protein-ligand complexes, which is notoriously difficult. We adopt an alternative approach that evaluates structural, rather than thermodynamic, stability. As bioactive molecules present a static binding mode, we devised dynamic undocking (DUck), a fast computational method to calculate the work necessary to reach a quasi-bound state at which the ligand has just broken the most important native contact with the receptor. This non-equilibrium property is surprisingly effective in virtual screening because true ligands form more-resilient interactions than decoys. Notably, DUck is orthogonal to docking and other 'thermodynamic' methods. We demonstrate the potential of the docking-undocking combination in a fragment screening against the molecular chaperone and oncology target Hsp90, for which we obtain novel chemotypes and a hit rate that approaches 40%.

Finding high quality beginning compounds is a critical job at the start of the lead generation stage for multi-target drug discovery (MTDD). Designing hybrid compounds as selective multitarget chemical entity is a challenge, opportunity, and new idea to better act against specific multiple targets. One hybrid molecule is formed by two (or more) pharmacophore group's participation. So, these new compounds often exhibit two or more activities going about as multi-target drugs (mtdrugs) and may have superior safety or efficacy. Application of integrating a range of information and sophisticated new in silico, bioinformatics, structural biology, pharmacogenomics methods may be useful to discover/design, and synthesis of the new hybrid molecules. In this regard, many rational and screening approaches have followed by medicinal chemists for the lead generation in MTDD. Here, we review some popular lead generation approaches that have been used for designing multiple ligands (DMLs). This paper focuses on dual- acting chemical entities that incorporate a part of two drugs or bioactive compounds to compose hybrid molecules. Also, it presents some of key concepts and limitations/strengths of lead generation methods by comparing combination framework method with screening approaches. Besides, a number of examples to represent applications of hybrid molecules in the drug discovery are included., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.)

Technologies for the engineering of biocatalysts for efficient synthesis of pharmaceutical targets have advanced dramatically over the last few years. Integration of computational methods for structural modeling, combined with high through put methods for expression and screening of biocatalysts and algorithms for mining experimental data, have allowed the creation of highly engineered biocatalysts for the efficient synthesis of pharmaceuticals. Methods for the synthesis of chiral alcohols and amines have been particularly successful, along with the creation of non-natural activities for such desirable reactions as cyclopropanation and esterification., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

In recent years, there has been an exponential rise in the number of reports describing synthetic methods that utilize catalytic sp 3 and sp 2 C-H bond activation. Many have emerged as powerful synthetic tools for accessing biologically active motifs. Indeed, application to C-C and C-heteroatom bond formation, provides new directives for the construction of new pharmaceutical entities. Herein, we highlight some recent novel C-H activation processes that exemplify the utility of these transformations in medicinal chemistry., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

For the last 15 years, kinetic target-guided syntheses, including in situ click chemistry, have been used as alternative methods to find ligands to therapeutically relevant proteins. In this review, a comprehensive survey of biological targets used in kinetic target-guided synthesis covers historical and recent examples. The chemical reactions employed and practical aspects, including controls, library sizes and product detection, are presented. A particular focus is on the reagents and warhead selection and design with a critical overview of the challenges encountered. As protein supply remains a key success factor, it appears that increased efforts should be taken toward miniaturization in order to expand the scope of this strategy and qualify it as a fully fledged drug discovery tool.

Drug discovery utilizes chemical biology and computational drug design approaches for the efficient identification and optimization of lead compounds. Chemical biology is mostly involved in the elucidation of the biological function of a target and the mechanism of action of a chemical modulator. On the other hand, computer-aided drug design makes use of the structural knowledge of either the target (structure-based) or known ligands with bioactivity (ligand-based) to facilitate the determination of promising candidate drugs. Various virtual screening techniques are now being used by both pharmaceutical companies and academic research groups to reduce the cost and time required for the discovery of a potent drug. Despite the rapid advances in these methods, continuous improvements are critical for future drug discovery tools. Advantages presented by structure-based and ligand-based drug design suggest that their complementary use, as well as their integration with experimental routines, has a powerful impact on rational drug design. In this article, we give an overview of the current computational drug design and their application in integrated rational drug development to aid in the progress of drug discovery research.

With the introduction of Quality by Design (QbD) to the pharmaceutical industry, there has been an increased focus on understanding the nature of particles and composites, with the aim of understanding and modeling how they interact in complex systems, leading to robust dosage forms. Particle characterization tools have evolved and now enable a greater level of understanding of powder systems and blends. Tools that can elucidate the size and shape of particulate systems can provide significantly more information about the nature of the particles being analyzed, than a conventional particle size measurement. Although accurate size and shape analysis has always been regarded as the "gold standard" in understanding the nature of particulate systems, neither imaging systems nor IT infrastructure was sufficiently developed to allow this to be performed with sufficient accuracy in a timely manner. The aim of this review is to provide an insight into developments in the field of size and shape analysis of pharmaceutical systems, and how these can now realistically be used as robust development tools. Examples of current uses of such technologies will be explored as well as investigating future applications such as combined image/spectroscopic analyses to track single components within blended systems., (© 2015 Wiley Periodicals, Inc. and the American Pharmacists Association.)

Terpenoids constitute a significant fraction of molecules produced by living organisms that have found use in medicine and other industries. Problems associated with their procurement and adaptation for human use can be solved using chemical synthesis, which is an increasingly economical option in the modern era of chemistry. This article documents, by way of individual case studies, strategies for reducing the time and cost of terpene synthesis for drug discovery. A major trend evident in recent syntheses is that complex terpenes are increasingly realistic starting points for both medicinal chemistry campaigns and large-scale syntheses, at least in the context of the academic laboratory, and this trend will likely penetrate the commercial sector in the near future.

Broadly speaking, the goals of the US Food and Drug Administration (FDA) special-designation programs--orphan, priority review, accelerated approval, and fast track--have been to expedite and sustain development and facilitate authorization of new medicines for unmet medical needs through so-called "push-pull" incentives. Although generally successful over time, their success has been confined to certain therapeutic areas and, within those areas, certain diseases. Times have changed. The research and development (R&D) burdens and public health urgency that acted as an impetus for the FDA to intervene more actively for certain disease areas are now broadly experienced across many disease areas. This betokens the need for the FDA to make designation and implementation decisions with a view that reaches beyond the immediate horizons of political expediency and patient advocacy to encompass the broader expanse of factors that now influence R&D decisions--global competitiveness, the needs of investors, emerging sponsors, and patient-focused drug development.

Pharmacological activity and druggability are two pivotal factors in drug innovation, which are respectively determined by the microscopic structure and macroscopic property of a molecule. Since structural optimization consists in a molecular operation in the space with multi-dimensions, and there exists a body of uncertainties for transduction from in vitro activity into in vivo pharmacological response. It is necessary for early stage in lead optimization to evaluate compound quality or efficiency using a kind of metrics containing multi-parameters. On the basis of the describing parameters of activity and druggability, this overview deals with the roles of thermodynamic signatures and binding kinetics of drug-receptor interactions in optimizing quality of compounds, signifying the significance in optimization of microscopic structures for drug discovery.

Lead compound optimization plays an important role in new drug discovery and development. The strategies for changing metabolic pathways can modulate pharmacokinetic properties, prolong the half life, improve metabolism stability and bioavailability of lead compounds. The strategies for changing metabolic pathways and improving metabolism stability are reviewed. These methods include blocking metabolic site, reduing lipophilicity, changing ring size, bioisosterism, and prodrug.

This article describes our experiences in creating a fully integrated, globally accessible, automated chemical synthesis laboratory. The goal of the project was to establish a fully integrated automated synthesis solution that was initially focused on minimizing the burden of repetitive, routine, rules-based operations that characterize more established chemistry workflows. The architecture was crafted to allow for the expansion of synthetic capabilities while also providing for a flexible interface that permits the synthesis objective to be introduced and manipulated as needed under the judicious direction of a remote user in real-time. This innovative central synthesis suite is herein described along with some case studies to illustrate the impact such a system is having in expanding drug discovery capabilities., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

This letter describes a multi-dimensional SAR campaign based on a potent, efficacious and selective GIRK1/2 activator (~10-fold versus GIRK1/4 and inactive on nonGIRK 1-containing GIRKs, GIRK 2 or GIRK2/3). Further chemical optimization through an iterative parallel synthesis effort identified multiple 'molecular switches' that modulated the mode of pharmacology from activator to inhibitor, as well as engendering varying selectivity profiles for GIRK1/2 and GIRK1/4. Importantly, these compounds were all inactive on nonGIRK1 containing GIRK channels. However, SAR was challenging as subtle structural modifications had large effects on both mode of pharmacology and GIRK1/2 and GIRK1/4 channel selectivity., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

Compound libraries and chemical synthesis play important roles in drug discovery and development, and efficient synthetic techniques can greatly facilitate the drug research. This review highlights the application of some efficient synthetic techniques in drug research including microwave chemistry, click chemistry, combinatorial chemistry, cascade reactions and multicomponent reactions, as well as the construction of diverse and drug-like compound libraries.

Chemistry groups involved in drug discovery continue to devote their efforts to improving compound design with the aim of identifying new drug candidates. Many crucial factors must be considered, including: chemical stability, synthetic difficulty, chemical complexity and diversity, ADMET properties, cost, chemical novelty and intellectual property issues, and 'biological appropriateness'. With regard to the latter point, natural products offer an outstanding source of biologically active molecules that provide many useful features that enable us to design innovative, biologically biased, synthetic compounds. This article outlines the recent approaches in this area and suggests a simple metric to assess synthetic compounds for natural product likeness., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

The tool chest of techniques, methodologies, and equipment for conducting parallel chemistry is larger than ever before. Improvements in the laboratory and developments in computational chemistry have enabled compound library design at the desks of medicinal chemists. This unit includes a brief background in combinatorial/parallel synthesis chemistry, along with a discussion of evolving technologies for both solid- and solution-phase chemistry. In addition, there are discussions on designing compound libraries, acquisition/procurement of compounds and/or reagents, the chemistry and equipment used for chemical production, purification, sample handling, and data analysis.

The design of lead and drug-like molecules with expected desired properties and feasible chemical synthesis is one of the main objectives of computational and medicinal chemists. Prediction of synthetic feasibility of de novo molecules is often achieved by the use of in-silico tools or by advices received from medicinal and to a lesser extent from computational chemists. However, the validation of predictive tools is often performed on selection of compounds from external databases. In this study, we compare the synthetic accessibility (SA) score predicted by SYLVIA and the score estimated by medicinal chemists who synthesized the molecules. Therefore, we solicited 11 bench-based medicinal and computational chemists to score 119 lead-like molecules synthesized by same medicinal chemists. Their scores were compared with score calculated from SYLVIA software. Irrespective of the starting material database, we obtained a good agreement between average of medicinal and computational chemist scores for the ensemble of compounds; as well as between all chemists and SYLVIA SA scores with a correlation of 0.7. Furthermore, analysis of the marketed drugs since 1970 shows some consistency in average SYLVIA SA scores. Compounds entered in different phases of clinical trials show some large variation in synthetic accessibility scores due to natural-derived molecular scaffolds. Here, we proposed that the selection of compounds based on synthetically accessibility should not be done solely by one individual chemist to avoid personal gut-feeling appreciation from its experience but by a group of medicinal and computational chemists. By assessing synthetic accessibility of hundreds of compounds synthesized by medicinal chemists, we show that SYLVIA can be used efficiently to rank and prioritize virtual compound libraries in drug discovery processes., (Copyright © 2012 Elsevier Masson SAS. All rights reserved.)

The ability to carry out simultaneous orthogonal exchange chemistries has opened new opportunities for increasing the numerical and structural diversity accessible to Dynamic Combinatorial Chemistry. We present proof-of-concept experiments demonstrating this concept is transferrable to resin-bound DCC, facilitating the generation and analysis of libraries with greater structural diversity., (This journal is © The Royal Society of Chemistry 2012)

The application of parallel synthesis is an efficient approach to explore the chemical space and to rapidly develop meaningful structure activity relationship (SAR) data for drug discovery programs. However, the effectiveness of the parallel synthesis requires a high throughput purification workflow that can process a large number of crude samples within a meaningful time frame. This paper describes a high throughput purification platform that has been adopted at Merck's Rahway research site. The platform includes the evaluation of crude samples, mass-directed HPLC purification, fraction analysis, compound registration, final compound purity assessment and assay distribution. Assisting with the sample tracking and the data management is the internally designed laboratory information management system, Light Automation Framework (LAF). Using this process and the tools described herein, the group has successfully achieved purities of 95% or greater for 90% of samples.

Natural products represent an important treasure box of biologically active molecules, from which many drug candidates have been sourced. The identification of the target proteins addressed by these natural products is a foremost goal for which new techniques are required. Activity-based protein profiling (ABPP), exploiting protein-reactive functional groups present in many natural products, offers unseen opportunities in this respect. This review article describes the current status of this field. Many examples are given for the annotation of biological target proteins of natural products containing epoxides, lactones, lactams, Michael acceptors, and other electrophilic groups. In addition, the development of probe molecules identified from biomimetic natural product libraries is discussed.

In 2001, a multidisciplinary team made of analytical scientists and statisticians at Sanofi-aventis has published a methodology which has governed, from that time, the transfers from R&D sites to Manufacturing sites of the release monographs. This article provides an overview of the recent adaptations brought to this original methodology taking advantage of our experience and the new regulatory framework, and, in particular, the risk management perspective introduced by ICH Q9. Although some alternate strategies have been introduced in our practices, the comparative testing one, based equivalence testing as statistical approach, remains the standard for assays lying on very critical quality attributes. This is conducted with the concern to control the most important consumer's risk involved at two levels in analytical decisions in the frame of transfer studies: risk, for the receiving laboratory, to take poor release decisions with the analytical method and risk, for the sending laboratory, to accredit such a receiving laboratory on account of its insufficient performances with the method. Among the enhancements to the comparative studies, the manuscript presents the process settled within our company for a better integration of the transfer study into the method life-cycle, just as proposals of generic acceptance criteria and designs for assay and related substances methods. While maintaining rigor and selectivity of the original approach, these improvements tend towards an increased efficiency in the transfer operations., (Copyright © 2011 Elsevier B.V. All rights reserved.)

Studies in the field of forensic pharmacology and toxicology would not be complete without some knowledge of the history of drug discovery, the various personalities involved, and the events leading to the development and introduction of new therapeutic agents. The first medicinal drugs came from natural sources and existed in the form of herbs, plants, roots, vines and fungi. Until the mid-nineteenth century nature's pharmaceuticals were all that were available to relieve man's pain and suffering. The first synthetic drug, chloral hydrate, was discovered in 1869 and introduced as a sedative-hypnotic; it is still available today in some countries. The first pharmaceutical companies were spin-offs from the textiles and synthetic dye industry and owe much to the rich source of organic chemicals derived from the distillation of coal (coal-tar). The first analgesics and antipyretics, exemplified by phenacetin and acetanilide, were simple chemical derivatives of aniline and p-nitrophenol, both of which were byproducts from coal-tar. An extract from the bark of the white willow tree had been used for centuries to treat various fevers and inflammation. The active principle in white willow, salicin or salicylic acid, had a bitter taste and irritated the gastric mucosa, but a simple chemical modification was much more palatable. This was acetylsalicylic acid, better known as Aspirin®, the first blockbuster drug. At the start of the twentieth century, the first of the barbiturate family of drugs entered the pharmacopoeia and the rest, as they say, is history., (Copyright © 2011 John Wiley & Sons, Ltd.)