Big pharma screening collections: more of the same or unique libraries? The AstraZeneca-Bayer Pharma AG case

Collaborative Virtual Screening Identifies a 2-Aryl-4-aminoquinazoline Series with Efficacy in an In Vivo Model of Trypanosoma cruzi Infection

Probing multiple proprietary pharmaceutical libraries in parallel via virtual screening allowed rapid expansion of the structure-activity relationship (SAR) around hit compounds with moderate efficacy against Trypanosoma cruzi, the causative agent of Chagas Disease. A potency-improving scaffold hop, followed by elaboration of the SAR via design guided by the output of the phenotypic virtual screening efforts, identified two promising hit compounds 54 and 85, which were profiled further in pharmacokinetic studies and in an in vivo model of T. cruzi infection. Compound 85 demonstrated clear reduction of parasitemia in the in vivo setting, confirming the interest in this series of 2-(pyridin-2-yl)quinazolines as potential anti-trypanosome treatments.

On drug discovery against infectious diseases and academic medicinal chemistry contributions

This perspective is an attempt to document the problems that medicinal chemists are facing in drug discovery. It is also trying to identify relevant/possible, research areas in which academics can have an impact and should thus be the subject of grant calls. Accordingly, it describes how hit discovery happens, how compounds to be screened are selected from available chemicals and the possible reasons for the recurrent paucity of useful/exploitable results reported. This is followed by the successful hit to lead stories leading to recent and original antibacterials which are, or about to be, used in human medicine. Then, illustrated considerations and suggestions are made on the possible inputs of academic medicinal chemists. This starts with the observation that discovering a "good" hit in the course of a screening campaign still rely on a lot of luck - which is within the reach of academics -, that the hit to lead process requires a lot of chemistry and that if public-private partnerships can be important throughout these stages, they are absolute requirements for clinical trials. Concerning suggestions to improve the current hit success rate, one academic input in organic chemistry would be to identify new and pertinent chemical space, design synthetic accesses to reach these and prepare the corresponding chemical libraries. Concerning hit to lead programs on a given target, if no new hits are available, previously reported leads along with new structural data can be pertinent starting points to design, prepare and assay original analogues. In conclusion, this text is an actual plea illustrating that, in many countries, academic research in medicinal chemistry should be more funded, especially in the therapeutic area neglected by the industry. At the least, such funds would provide the intensive to secure series of hopefully relevant chemical entities which appears to often lack when considering the results of academic as well as industrial screening campaigns.

A machine learning model for classifying G-protein-coupled receptors as agonists or antagonists

Background

G-protein coupled receptors (GPCRs) sense and transmit extracellular signals into the intracellular machinery by regulating G proteins. GPCR malfunctions are associated with a variety of signaling-related diseases, including cancer and diabetes; at least a third of the marketed drugs target GPCRs. Thus, characterization of their signaling and regulatory mechanisms is crucial for the development of effective drugs.

Results

In this study, we developed a machine learning model to identify GPCR agonists and antagonists. We designed two-step prediction models: the first model identified the ligands binding to GPCRs and the second model classified the ligands as agonists or antagonists. Using 990 selected subset features from 5270 molecular descriptors calculated from 4590 ligands deposited in two drug databases, our model classified non-ligands, agonists, and antagonists of GPCRs, and achieved an area under the ROC curve (AUC) of 0.795, sensitivity of 0.716, specificity of 0.744, and accuracy of 0.733. In addition, we verified that 70% (44 out of 63) of FDA-approved GPCR-targeting drugs were correctly classified into their respective groups.

Conclusions

Studies of ligand–GPCR interaction recognition are important for the characterization of drug action mechanisms. Our GPCR–ligand interaction prediction model can be employed in the pharmaceutical sciences for the efficient virtual screening of putative GPCR-binding agonists and antagonists.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04877-7.

The European Lead Factory: An updated HTS compound library for innovative drug discovery

Through the European Lead Factory model, industry-standard high-throughput screening and hit validation are made available to academia, small and medium-sized enterprises, charity organizations, patient foundations, and participating pharmaceutical companies. The compound collection used for screening is built from a unique diversity of sources. It brings together compounds from companies with different therapeutic area heritages and completely new compounds from library synthesis. This generates structural diversity and combines molecules with complementary physicochemical properties. In 2019, the screening library was updated to enable another 5 years of running innovative drug discovery projects. Here, we investigate the physicochemical and diversity properties of the updated compound collection. We show that it is highly diverse, drug-like, and complementary to commercial screening libraries.

Artificial intelligence in drug discovery: recent advances and future perspectives

Introduction: Artificial intelligence (AI) has inspired computer-aided drug discovery. The widespread adoption of machine learning, in particular deep learning, in multiple scientific disciplines, and the advances in computing hardware and software, among other factors, continue to fuel this development. Much of the initial skepticism regarding applications of AI in pharmaceutical discovery has started to vanish, consequently benefitting medicinal chemistry.Areas covered: The current status of AI in chemoinformatics is reviewed. The topics discussed herein include quantitative structure-activity/property relationship and structure-based modeling, de novo molecular design, and chemical synthesis prediction. Advantages and limitations of current deep learning applications are highlighted, together with a perspective on next-generation AI for drug discovery.Expert opinion: Deep learning-based approaches have only begun to address some fundamental problems in drug discovery. Certain methodological advances, such as message-passing models, spatial-symmetry-preserving networks, hybrid de novo design, and other innovative machine learning paradigms, will likely become commonplace and help address some of the most challenging questions. Open data sharing and model development will play a central role in the advancement of drug discovery with AI.

Public-Private Partnerships: Compound and Data Sharing in Drug Discovery and Development

Collaborative efforts between public and private entities such as academic institutions, governments, and pharmaceutical companies form an integral part of scientific research, and notable instances of such initiatives have been created within the life science community. Several examples of alliances exist with the broad goal of collaborating toward scientific advancement and improved public welfare. Such collaborations can be essential in catalyzing breaking areas of science within high-risk or global public health strategies that may have otherwise not progressed. A common term used to describe these alliances is public-private partnership (PPP). This review discusses different aspects of such partnerships in drug discovery/development and provides example applications as well as successful case studies. Specific areas that are covered include PPPs for sharing compounds at various phases of the drug discovery process-from compound collections for hit identification to sharing clinical candidates. Instances of PPPs to support better data integration and build better machine learning models are also discussed. The review also provides examples of PPPs that address the gap in knowledge or resources among involved parties and advance drug discovery, especially in disease areas with unfulfilled and/or social needs, like neurological disorders, cancer, and neglected and rare diseases.

Collaborative virtual screening to elaborate an imidazo[1,2-a]pyridine hit series for visceral leishmaniasis

An innovative pre-competitive virtual screening collaboration was engaged to validate and subsequently explore an imidazo[1,2-a]pyridine screening hit for visceral leishmaniasis. In silico probing of five proprietary pharmaceutical company libraries enabled rapid expansion of the hit chemotype, alleviating initial concerns about the core chemical structure while simultaneously improving antiparasitic activity and selectivity index relative to the background cell line. Subsequent hit optimization informed by the structure–activity relationship enabled by this virtual screening allowed thorough investigation of the pharmacophore, opening avenues for further improvement and optimization of the chemical series.

Ligand-based similarity screening of proprietary pharmaceutical company libraries enables rapid hit to lead investigation of a chemotype with anti-leishmania activity.

Neuraldecipher - reverse-engineering extended-connectivity fingerprints (ECFPs) to their molecular structures

Protecting molecular structures from disclosure against external parties is of great relevance for industrial and private associations, such as pharmaceutical companies. Within the framework of external collaborations, it is common to exchange datasets by encoding the molecular structures into descriptors. Molecular fingerprints such as the extended-connectivity fingerprints (ECFPs) are frequently used for such an exchange, because they typically perform well on quantitative structure-activity relationship tasks. ECFPs are often considered to be non-invertible due to the way they are computed. In this paper, we present a fast reverse-engineering method to deduce the molecular structure given revealed ECFPs. Our method includes the Neuraldecipher, a neural network model that predicts a compact vector representation of compounds, given ECFPs. We then utilize another pre-trained model to retrieve the molecular structure as SMILES representation. We demonstrate that our method is able to reconstruct molecular structures to some extent, and improves, when ECFPs with larger fingerprint sizes are revealed. For example, given ECFP count vectors of length 4096, we are able to correctly deduce up to 69% of molecular structures on a validation set (112 K unique samples) with our method.

Chemical Screening of Nuclear Receptor Modulators

Nuclear receptors are ligand-inducible transcriptional factors that control multiple biological phenomena, including proliferation, differentiation, reproduction, metabolism, and the maintenance of homeostasis. Members of the nuclear receptor superfamily have marked structural and functional similarities, and their domain functionalities and regulatory mechanisms have been well studied. Various modulators of nuclear receptors, including agonists and antagonists, have been developed as tools for elucidating nuclear receptor functions and also as drug candidates or lead compounds. Many assay systems are currently available to evaluate the modulation of nuclear receptor functions, and are useful as screening tools in the discovery and development of new modulators. In this review, we cover the chemical screening methods for nuclear receptor modulators, focusing on assay methods and chemical libraries for screening. We include some recent examples of the discovery of nuclear receptor modulators.

Oncolytic Adenoviruses: Strategies for Improved Targeting and Specificity

Cancer is a major health problem. Most of the treatments exhibit systemic toxicity, as they are not targeted or specific to cancerous cells and tumors. Adenoviruses are very promising gene delivery vectors and have immense potential to deliver targeted therapy. Here, we review a wide range of strategies that have been tried, tested, and demonstrated to enhance the specificity of oncolytic viruses towards specific cancer cells. A combination of these strategies and other conventional therapies may be more effective than any of those strategies alone.

Applications of Deep-Learning in Exploiting Large-Scale and Heterogeneous Compound Data in Industrial Pharmaceutical Research

In recent years, the development of high-throughput screening (HTS) technologies and their establishment in an industrialized environment have given scientists the possibility to test millions of molecules and profile them against a multitude of biological targets in a short period of time, generating data in a much faster pace and with a higher quality than before. Besides the structure activity data from traditional bioassays, more complex assays such as transcriptomics profiling or imaging have also been established as routine profiling experiments thanks to the advancement of Next Generation Sequencing or automated microscopy technologies. In industrial pharmaceutical research, these technologies are typically established in conjunction with automated platforms in order to enable efficient handling of screening collections of thousands to millions of compounds. To exploit the ever-growing amount of data that are generated by these approaches, computational techniques are constantly evolving. In this regard, artificial intelligence technologies such as deep learning and machine learning methods play a key role in cheminformatics and bio-image analytics fields to address activity prediction, scaffold hopping, de novo molecule design, reaction/retrosynthesis predictions, or high content screening analysis. Herein we summarize the current state of analyzing large-scale compound data in industrial pharmaceutical research and describe the impact it has had on the drug discovery process over the last two decades, with a specific focus on deep-learning technologies.

Bacterial production and direct functional screening of expanded molecular libraries for discovering inhibitors of protein aggregation

Protein misfolding and aggregation are associated with a many human disorders, including Alzheimer's and Parkinson's diseases. Toward increasing the effectiveness of early-stage drug discovery for these conditions, we report a bacterial platform that enables the biosynthesis of molecular libraries with expanded diversities and their direct functional screening for discovering protein aggregation inhibitors. We illustrate this approach by performing, what is to our knowledge, the largest functional screen of small-size molecular entities described to date. We generated a combinatorial library of ~200 million drug-like, cyclic peptides and rapidly screened it for aggregation inhibitors against the amyloid-β peptide (Aβ42), linked to Alzheimer's disease. Through this procedure, we identified more than 400 macrocyclic compounds that efficiently reduce Aβ42 aggregation and toxicity in vitro and in vivo. Finally, we applied a combination of deep sequencing and mutagenesis analyses to demonstrate how this system can rapidly determine structure-activity relationships and define consensus motifs required for bioactivity.

Looking beyond the hype: Applied AI and machine learning in translational medicine

Big data problems are becoming more prevalent for laboratory scientists who look to make clinical impact. A large part of this is due to increased computing power, in parallel with new technologies for high quality data generation. Both new and old techniques of artificial intelligence (AI) and machine learning (ML) can now help increase the success of translational studies in three areas: drug discovery, imaging, and genomic medicine. However, ML technologies do not come without their limitations and shortcomings. Current technical limitations and other limitations including governance, reproducibility, and interpretation will be discussed in this article. Overcoming these limitations will enable ML methods to be more powerful for discovery and reduce ambiguity within translational medicine, allowing data-informed decision-making to deliver the next generation of diagnostics and therapeutics to patients quicker, at lowered costs, and at scale.

Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design

In medicinal chemistry programs it is key to design and make compounds that are efficacious and safe. This is a long, complex, and difficult multiparameter optimization process, often including several properties with orthogonal trends. New methods for the automated design of compounds against profiles of multiple properties are thus of great value. Here we present a fragment-based reinforcement learning approach based on an actor-critic model, for the generation of novel molecules with optimal properties. The actor and the critic are both modeled with bidirectional long short-term memory (LSTM) networks. The AI method learns how to generate new compounds with desired properties by starting from an initial set of lead molecules and then improving these by replacing some of their fragments. A balanced binary tree based on the similarity of fragments is used in the generative process to bias the output toward structurally similar molecules. The method is demonstrated by a case study showing that 93% of the generated molecules are chemically valid and more than a third satisfy the targeted objectives, while there were none in the initial set.

An approach towards enhancement of a screening library: The Next Generation Library Initiative (NGLI) at Bayer - against all odds?

Pharmaceutical companies often refer to 'screening their library' when performing high-throughput screening (HTS) on a corporate compound collection to identify lead structures for small-molecule drug discovery programs. Characteristics of such a library, including the size, chemical space covered, and physicochemical properties, often determine the success of a screening campaign. Therefore, strategies to maintain and enhance the overall quality of screening collections are crucial to stay competitive and to cope with the 'novelty erosion' that is observed gradually. The Next Generation Library Initiative (NGLI), the enhancement of Bayer's HTS collection by 500000 newly designed compounds within 5 years, is addressing exactly this challenge. Here, we describe this collaborative project, which involves all internal medicinal chemists in a crowd-sourcing approach, as well as selected external partners, to reach this ambitious goal.

Chemical probes and drug leads from advances in synthetic planning and methodology

Screening of small-molecule libraries is a productive method for identifying both chemical probes of disease-related targets and potential starting points for drug discovery. In this article, we focus on strategies such as diversity-oriented synthesis that aim to explore novel areas of chemical space efficiently by populating small-molecule libraries with compounds containing structural features that are typically under-represented in commercially available screening collections. Drawing from more than a decade's worth of examples, we highlight how the design and synthesis of such libraries have been enabled by modern synthetic chemistry, and we illustrate the impact of the resultant chemical probes and drug leads in a wide range of diseases.

Big Data in Drug Discovery

Interpretation of Big Data in the drug discovery community should enhance project timelines and reduce clinical attrition through improved early decision making. The issues we encounter start with the sheer volume of data and how we first ingest it before building an infrastructure to house it to make use of the data in an efficient and productive way. There are many problems associated with the data itself including general reproducibility, but often, it is the context surrounding an experiment that is critical to success. Help, in the form of artificial intelligence (AI), is required to understand and translate the context. On the back of natural language processing pipelines, AI is also used to prospectively generate new hypotheses by linking data together. We explain Big Data from the context of biology, chemistry and clinical trials, showcasing some of the impressive public domain sources and initiatives now available for interrogation.

Innovation in Small-Molecule-Druggable Chemical Space: Where are the Initial Modulators of New Targets Published?

It is well-established that the number of publications of novel small molecule modulators, and their associated targets, has increased over the years. This work focuses on publishing trends over the years with a particular focus on the comparison between patents and scientific literature which is accessible via the ChEMBL and GOSTAR databases. More precisely, the patents and scientific literature associated with bioactive molecules and their target annotations have been compared to identify where novelty (in the meaning of the first modulator of a protein target) originated from. Comparing the published date of the first small molecule modulator published in literature and patents for a particular target (with either identical or different structure) shows that modulators are usually published in both scientific literature and in patents (45%), or in scientific literature alone (51%), but rarely in patents only. When looking at the time when first modulators are published in both sources, 65% of the time they are disseminated in literature first. Finally, when analyzing just the novel small molecule modulators, regardless of the protein targets they have been published with, those structures representing novel chemistry tend to be published in patents first 61% of the time.

CHEMGENIE: integration of chemogenomics data for applications in chemical biology

Increasing amounts of biological data are accumulating in the pharmaceutical industry and academic institutions. However, data does not equal actionable information, and guidelines for appropriate data capture, harmonization, integration, mining, and visualization need to be established to fully harness its potential. Here, we describe ongoing efforts at Merck & Co. to structure data in the area of chemogenomics. We are integrating complementary data from both internal and external data sources into one chemogenomics database (Chemical Genetic Interaction Enterprise; CHEMGENIE). Here, we demonstrate how this well-curated database facilitates compound set design, tool compound selection, target deconvolution in phenotypic screening, and predictive model building.

Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB)

Neglected disease drug discovery is generally poorly funded compared with major diseases and hence there is an increasing focus on collaboration and precompetitive efforts such as public-private partnerships (PPPs). The More Medicines for Tuberculosis (MM4TB) project is one such collaboration funded by the EU with the goal of discovering new drugs for tuberculosis. Collaborative Drug Discovery has provided a commercial web-based platform called CDD Vault which is a hosted collaborative solution for securely sharing diverse chemistry and biology data. Using CDD Vault alongside other commercial and free cheminformatics tools has enabled support of this and other large collaborative projects, aiding drug discovery efforts and fostering collaboration. We will describe CDD's efforts in assisting with the MM4TB project.

BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry

The increasing volume of biomedical data in chemistry and life sciences requires the development of new methods and approaches for their handling. Here, we briefly discuss some challenges and opportunities of this fast growing area of research with a focus on those to be addressed within the BIGCHEM project. The article starts with a brief description of some available resources for “Big Data” in chemistry and a discussion of the importance of data quality. We then discuss challenges with visualization of millions of compounds by combining chemical and biological data, the expectations from mining the “Big Data” using advanced machine‐learning methods, and their applications in polypharmacology prediction and target de‐convolution in phenotypic screening. We show that the efficient exploration of billions of molecules requires the development of smart strategies. We also address the issue of secure information sharing without disclosing chemical structures, which is critical to enable bi‐party or multi‐party data sharing. Data sharing is important in the context of the recent trend of “open innovation” in pharmaceutical industry, which has led to not only more information sharing among academics and pharma industries but also the so‐called “precompetitive” collaboration between pharma companies. At the end we highlight the importance of education in “Big Data” for further progress of this area.

Using cultured endothelial cells to study endothelial barrier dysfunction: Challenges and opportunities

Despite considerable progress in the understanding of endothelial barrier regulation and the identification of approaches that have the potential to improve endothelial barrier function, no drug- or stem cell-based therapy is presently available to reverse the widespread vascular leak that is observed in acute respiratory distress syndrome (ARDS) and sepsis. The translational gap suggests a need to develop experimental approaches and tools that better mimic the complex environment of the microcirculation in which the vascular leak develops. Recent studies have identified several elements of this microenvironment. Among these are composition and stiffness of the extracellular matrix, fluid shear stress, interaction of endothelial cells (ECs) with pericytes, oxygen tension, and the combination of toxic and mechanic injurious stimuli. Development of novel cell culture techniques that integrate these elements would allow in-depth analysis of EC biology that closely approaches the (patho)physiological conditions in situ. In parallel, techniques to isolate organ-specific ECs, to define EC heterogeneity in its full complexity, and to culture patient-derived ECs from inducible pluripotent stem cells or endothelial progenitor cells are likely to advance the understanding of ARDS and lead to development of therapeutics. This review 1) summarizes the advantages and pitfalls of EC cultures to study vascular leak in ARDS, 2) provides an overview of elements of the microvascular environment that can directly affect endothelial barrier function, and 3) discusses alternative methods to bridge the gap between basic research and clinical application with the intent of improving the translational value of present EC culture approaches.

Open innovation in early drug discovery: roadmaps and roadblocks

Open innovation in pharmaceutical R&D evolved from a triple helix of convergent paradigm shifts in academic, industrial and government research sectors. The birth of the biotechnology sector catalyzed shifts in location dynamics that led to the first wave of open innovation in pharmaceutical R&D between big pharma and startup companies. The National Institutes of Health (NIH) Roadmap was a crucial inflection point that set the stage for a new wave of open innovation models between pharmaceutical companies and universities that have the potential to transform the pharmaceutical R&D landscape. We highlight the attributes of leading protected open innovation models that foster the sharing of proprietary small molecule collections by lowering the risk of premature escape of intellectual property, particularly structure-activity data.

Highlights from SelectBio 2015: Academic Drug Discovery Conference, Cambridge, UK, 19-20 May 2015

The SelectBio 2015: Academic Drug Discovery Conference was held in Cambridge, UK, on 19-20 May 2015. Building on the success of academic drug discovery events in the USA, this conference aimed to showcase the exciting new research emerging from academic drug discovery and to help bridge the gap between basic research and commercial application. At the event the authors heard from a number of speakers on a broad array of topics, from partnering models for academia and industry to novel drug discovery approaches across various therapeutic areas, with a few talks, such as those by Susanne Muller-Knapp (Structure Genomics Consortium, Oxford University, Oxford, UK) and Julian Blagg (Institute of Cancer Research, UK), covering both remits, by highlighting a number of such partnerships and then delving into some case studies. The conference concluded with a heated debate on whether phenotypic discovery should be favored over targeted discovery in academia and pharma, in a panel discussion chaired by Roland Wolkowicz (San Diego State University, USA).

A new paradigm for navigating compound property related drug attrition

Improving the efficiency of drug discovery remains a major focus for the pharmaceutical industry. Toxicity accounts for 90% of withdrawals and major early-stage terminations relate to suboptimal efficacy and safety. Traditional oral drug space is well defined with respect to physicochemical properties and ADMET risks but increased focus on ligand-lipophilicity efficiency, maximizing enthalpy contributions and new target classes challenge this paradigm. A hybrid space has been identified that combines physical properties and key interactions attributable to drug transporters. A novel algorithm is proposed that incorporates drug-transporter interactions and its utility evaluated against popular ligand efficiency indices. Simply reducing the bulk properties of compounds can exchange one problem for another and creates high-risk areas that challenge the successful delivery from a balanced portfolio.

Utilizing diversity-oriented synthesis in antimicrobial drug discovery

The development of resistance to existing antimicrobials has created a threat to human health that is not being addressed through our current drug pipeline. Limitations with the use of commercial vendor libraries and natural products have created a need for new types of small molecules to be screened in antimicrobial assays. Diversity oriented synthesis (DOS) is a strategy for the efficient generation of compound collections with a high degree of structural diversity. Diversity-oriented synthesis molecules occupy the middle ground of both complexity and efficiency of synthesis between natural products and commercial libraries. In this review we focus upon the use of diversity-oriented synthesis compound collections for the discovery of new antimicrobial agents.

Assessing HTS performance using BioAssay Ontology: screening and analysis of a bacterial phospho-N-acetylmuramoyl-pentapeptide translocase campaign

With the public availability of biochemical assays and screening data constantly increasing, new applications for data mining and method analysis are evolving in parallel. One example is BioAssay Ontology (BAO) for systematic classification of assays based on screening setup and metadata annotations. In this article we report a high-throughput screening (HTS) against phospho-N-acetylmuramoyl-pentapeptide translocase (MraY), an attractive antibacterial drug target involved in peptidoglycan synthesis. The screen resulted in novel chemistry identification using a fluorescence resonance energy transfer assay. To address a subset of the false positive hits, a frequent hitter analysis was performed using an approach in which MraY hits were compared with hits from similar assays, previously used for HTS. The MraY assay was annotated according to BAO and three internal reference assays, using a similar assay design and detection technology, were identified. Analyzing the assays retrospectively, it was clear that both MraY and the three reference assays all showed a high false positive rate in the primary HTS assays. In the case of MraY, false positives were efficiently identified by applying a method to correct for compound interference at the hit-confirmation stage. Frequent hitter analysis based on the three reference assays with similar assay method identified additional false actives in the primary MraY assay as frequent hitters. This article demonstrates how assays annotated using BAO terms can be used to identify closely related reference assays, and that analysis based on these assays clearly can provide useful data to influence assay design, technology, and screening strategy.

Screening technologies for small molecule discovery: the state of the art

Screening, high-throughput screening, and ultra-high-throughput screening are all really just points on a spectrum that represent differing applications of the same process: the creation of biologically relevant assays that are relevant, reproducible, reliable, and robust. Whether the discovery program is developing a pharmaceutical, an academic probe, cosmetics, pesticides, or a toxicity monitoring assay, the development of a screen focuses on generating a method that will reliably deliver reproducible results over a period of weeks, months, or years and that will generate consistent results for every test along the way. This review provides both historical perspective on how this unique scientific discipline evolved and commentary on the current state of the art technologies and techniques.

Increasing the delivery of next generation therapeutics from high throughput screening libraries

The pharmaceutical industry has historically relied on high throughput screening as a cornerstone to identify chemical equity for drug discovery projects. However, with pharmaceutical companies moving through a phase of diminished returns and alternative hit identification strategies proving successful, it is more important than ever to understand how this approach can be used more effectively to increase the delivery of next generation therapeutics from high throughput screening libraries. There is a wide literature that describes HTS and fragment based screening approaches which offer clear direction on the process for these two distinct activities. However, few people have considered how best to identify medium to low molecular weight compounds from large diversity screening sets and increase downstream success.

Addressing the Right Targets in Oncology

Translating existing and emerging knowledge of cancer biology into effective novel therapies remains a great challenge in drug discovery. A firm understanding of the target biology, confidence in the supporting preclinical research, and access to diverse chemical matter is required to lower attrition rates and prosecute targets effectively. Understanding past successes and failures will aid in refining this process to deliver further therapeutic benefit to patients. In this review, we suggest that early oncology drug discovery should focus on selection and prosecution of cancer targets with strong disease biology rather than on more chemically “druggable” targets with only modest disease-linkage. This approach offers higher potential benefit but also increases the need for innovative and alternative approaches. These include using different methods to validate novel targets and identify chemical matter, as well as raising the standards and our interpretation of the scientific literature. The combination of skills required for this emphasizes the need for broader early collaborations between academia and industry.

Bigger data, collaborative tools and the future of predictive drug discovery

Over the past decade we have seen a growth in the provision of chemistry data and cheminformatics tools as either free websites or software as a service commercial offerings. These have transformed how we find molecule-related data and use such tools in our research. There have also been efforts to improve collaboration between researchers either openly or through secure transactions using commercial tools. A major challenge in the future will be how such databases and software approaches handle larger amounts of data as it accumulates from high throughput screening and enables the user to draw insights, enable predictions and move projects forward. We now discuss how information from some drug discovery datasets can be made more accessible and how privacy of data should not overwhelm the desire to share it at an appropriate time with collaborators. We also discuss additional software tools that could be made available and provide our thoughts on the future of predictive drug discovery in this age of big data. We use some examples from our own research on neglected diseases, collaborations, mobile apps and algorithm development to illustrate these ideas.

The Joint European Compound Library: boosting precompetitive research

The Joint European Compound Library (JECL) is a new high-throughput screening collection aimed at driving precompetitive drug discovery and target validation. The JECL has been established with a core of over 321,000 compounds from the proprietary collections of seven pharmaceutical companies and will expand to around 500,000 compounds. Here, we analyse the physicochemical profile and chemical diversity of the core collection, showing that the collection is diverse and has a broad spectrum of predicted biological activity. We also describe a model for sharing compound information from multiple proprietary collections, enabling diversity and quality analysis without disclosing structures. The JECL is available for screening at no cost to European academic laboratories and SMEs through the IMI European Lead Factory (http://www.europeanleadfactory.eu/).

Screening strategies to identify new chemical diversity for drug development to treat kinetoplastid infections

The Drugs for Neglected Diseases initiative (DNDi) has defined and implemented an early discovery strategy over the last few years, in fitting with its virtual R&D business model. This strategy relies on a medium- to high-throughput phenotypic assay platform to expedite the screening of compound libraries accessed through its collaborations with partners from the pharmaceutical industry. We review the pragmatic approaches used to select compound libraries for screening against kinetoplastids, taking into account screening capacity. The advantages, limitations and current achievements in identifying new quality series for further development into preclinical candidates are critically discussed, together with attractive new approaches currently under investigation.