Novel web-based tools combining chemistry informatics, biology and social networks for drug discovery

A data science roadmap for open science organizations engaged in early-stage drug discovery

The Structural Genomics Consortium is an international open science research organization with a focus on accelerating early-stage drug discovery, namely hit discovery and optimization. We, as many others, believe that artificial intelligence (AI) is poised to be a main accelerator in the field. The question is then how to best benefit from recent advances in AI and how to generate, format and disseminate data to enable future breakthroughs in AI-guided drug discovery. We present here the recommendations of a working group composed of experts from both the public and private sectors. Robust data management requires precise ontologies and standardized vocabulary while a centralized database architecture across laboratories facilitates data integration into high-value datasets. Lab automation and opening electronic lab notebooks to data mining push the boundaries of data sharing and data modeling. Important considerations for building robust machine-learning models include transparent and reproducible data processing, choosing the most relevant data representation, defining the right training and test sets, and estimating prediction uncertainty. Beyond data-sharing, cloud-based computing can be harnessed to build and disseminate machine-learning models. Important vectors of acceleration for hit and chemical probe discovery will be (1) the real-time integration of experimental data generation and modeling workflows within design-make-test-analyze (DMTA) cycles openly, and at scale and (2) the adoption of a mindset where data scientists and experimentalists work as a unified team, and where data science is incorporated into the experimental design.

Target-based discovery of antagonists of the tick (Rhipicephalus microplus) kinin receptor identifies small molecules that inhibit midgut contractions

BACKGROUND

A GPCR (G protein-coupled receptor) target-based approach was applied to identify antagonists of the arthropod-specific tick kinin receptor. These small molecules were expected to reproduce the detrimental phenotypic effects that had been observed in Rhipicephalus microplus females when the kinin receptor was silenced by RNA interference. Rhipicephalus microplus, the southern cattle tick, cattle fever tick, or Asian blue tick, is the vector of pathogenic microorganisms causing the deadly bovine babesiosis and anaplasmosis. The widespread resistance to acaricides in tick populations worldwide emphasizes that exploring novel targets for effective tick control is imperative.

RESULTS

Fifty-three structural analogs of previously identified tick kinin antagonists were screened in a 'dual-addition' calcium fluorescence assay using a CHO-K1 cell line expressing the tick kinin receptor. Seven molecules were validated as non-cytotoxic antagonists, four of which were partial (SACC-0428764, SACC-0428780, SACC-0428800, and SACC-0428803), and three were full antagonists (SACC-0428799, SACC-0428801, and SACC-0428815). Four of these antagonists (SACC-0428764, SACC-0428780, SACC-0428799, and SACC-0428815) also inhibited the tick midgut contractions induced by the myotropic kinin agonist analog 1728, verifying their antagonistic bioactivity. The small molecules were tested on recombinant human neurokinin (NK) receptors, the one most similar to the invertebrate kinin receptors. Most molecules were inhibitors of the NK1 receptor, except SACC-0412066, a previously identified tick kinin receptor antagonist, which inhibited the NK1 receptor only at the highest concentration tested (25 μm). None of the molecules inhibited the NK3 human receptor.

CONCLUSION

Molecules identified through this approach could be useful probes for studying the tick kinin signaling system and midgut physiology. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

System biology approaches for drug repurposing

Drug repurposing, or drug repositioning, refers to the identification of alternative therapeutic applications for established medications that go beyond their initial indications. This strategy has becoming increasingly popular since it has the potential to significantly reduce the overall costs of drug development by around $300 million. System biology methodologies have been employed to facilitate medication repurposing, encompassing computational techniques such as signature matching and network-based strategies. These techniques utilize pre-existing drug-related data types and databases to find prospective repurposed medications that have minimal or acceptable harmful effects on patients. The primary benefit of medication repurposing in comparison to drug development lies in the fact that approved pharmaceuticals have already undergone multiple phases of clinical studies, thereby possessing well-established safety and pharmacokinetic properties. Utilizing system biology methodologies in medication repurposing offers the capacity to expedite the discovery of viable candidates for drug repurposing and offer novel perspectives for structure-based drug design.

BMaps: A Web Application for Fragment-Based Drug Design and Compound Binding Evaluation

Fragment-based drug design uses data about where, and how strongly, small chemical fragments bind to proteins, to assemble new drug molecules. Over the past decade, we have been successfully using fragment data, derived from thermodynamically rigorous Monte Carlo fragment-protein binding simulations, in dozens of preclinical drug programs. However, this approach has not been available to the broader research community because of the cost and complexity of doing simulations and using design tools. We have developed a web application, called BMaps, to make fragment-based drug design widely available with greatly simplified user interfaces. BMaps provides access to a large repository (>550) of proteins with 100s of precomputed fragment maps, druggable hot spots, and high-quality water maps. Users can also employ their own structures or those from the Protein Data Bank and AlphaFold DB. Multigigabyte data sets are searched to find fragments in bondable orientations, ranked by a binding-free energy metric. The designers use this to select modifications that improve affinity and other properties. BMaps is unique in combining conventional tools such as docking and energy minimization with fragment-based design, in a very easy to use and automated web application. The service is available at https://www.boltzmannmaps.com.

Biomaterialomics: Data science-driven pathways to develop fourth-generation biomaterials

Conventional approaches to developing biomaterials and implants require intuitive tailoring of manufacturing protocols and biocompatibility assessment. This leads to longer development cycles, and high costs. To meet existing and unmet clinical needs, it is critical to accelerate the production of implantable biomaterials, implants and biomedical devices. Building on the Materials Genome Initiative, we define the concept 'biomaterialomics' as the integration of multi-omics data and high-dimensional analysis with artificial intelligence (AI) tools throughout the entire pipeline of biomaterials development. The Data Science-driven approach is envisioned to bring together on a single platform, the computational tools, databases, experimental methods, machine learning, and advanced manufacturing (e.g., 3D printing) to develop the fourth-generation biomaterials and implants, whose clinical performance will be predicted using 'digital twins'. While analysing the key elements of the concept of 'biomaterialomics', significant emphasis has been put forward to effectively utilize high-throughput biocompatibility data together with multiscale physics-based models, E-platform/online databases of clinical studies, data science approaches, including metadata management, AI/ Machine Learning (ML) algorithms and uncertainty predictions. Such integrated formulation will allow one to adopt cross-disciplinary approaches to establish processing-structure-property (PSP) linkages. A few published studies from the lead author's research group serve as representative examples to illustrate the formulation and relevance of the 'Biomaterialomics' approaches for three emerging research themes, i.e. patient-specific implants, additive manufacturing, and bioelectronic medicine. The increased adaptability of AI/ML tools in biomaterials science along with the training of the next generation researchers in data science are strongly recommended. STATEMENT OF SIGNIFICANCE: This leading opinion review paper emphasizes the need to integrate the concepts and algorithms of the data science with biomaterials science. Also, this paper emphasizes the need to establish a mathematically rigorous cross-disciplinary framework that will allow a systematic quantitative exploration and curation of critical biomaterials knowledge needed to drive objectively the innovation efforts within a suitable uncertainty quantification framework, as embodied in 'biomaterialomics' concept, which integrates multi-omics data and high-dimensional analysis with artificial intelligence (AI) tools, like machine learning. The formulation of this approach has been demonstrated for patient-specific implants, additive manufacturing, and bioelectronic medicine.

MOFSocialNet: Exploiting Metal-Organic Framework Relationships via Social Network Analysis

The number of metal-organic frameworks (MOF) as well as the number of applications of this material are growing rapidly. With the number of characterized compounds exceeding 100,000, manual sorting becomes impossible. At the same time, the increasing computer power and established use of automated machine learning approaches makes data science tools available, that provide an overview of the MOF chemical space and support the selection of suitable MOFs for a desired application. Among the different data science tools, graph theory approaches, where data generated from numerous real-world applications is represented as a graph (network) of interconnected objects, has been widely used in a variety of scientific fields such as social sciences, health informatics, biological sciences, agricultural sciences and economics. We describe the application of a particular graph theory approach known as social network analysis to MOF materials and highlight the importance of community (group) detection and graph node centrality. In this first application of the social network analysis approach to MOF chemical space, we created MOFSocialNet. This social network is based on the geometrical descriptors of MOFs available in the CoRE-MOFs database. MOFSocialNet can discover communities with similar MOFs structures and identify the most representative MOFs within a given community. In addition, analysis of MOFSocialNet using social network analysis methods can predict MOF properties more accurately than conventional ML tools. The latter advantage is demonstrated for the prediction of gas storage properties, the most important property of these porous reticular networks.

Synthesis of Substituted -N-(5-((7-Methyl-2-Oxo-2H-Chromen-4-yl)- Methyl)-1,3,4-Thiadiazol-2-yl)-Benzamide Derivatives Using TBTU as Coupling Agent and their Evaluation for Anti Tubercular Activity

Abstract:

Tuberculosis remains a highly infectious disease across the world. In the identification of new antitubercular agents, coumarin clubbed thiadiazole amides have been synthesized and evaluated for in vitro antitubercular activity. Owing to the growing concern of chemicals and their impact on the environment, greener and faster reaction conditions needed to be incorporated. Therefore, we used TBTU as a coupling reagent for efficient and facile synthesis of substituted-N-(5-((7-methyl-2-oxo-2Hchromes- 4-yl)-methyl)-1,3, 4-thiadiazol-2-yl)-benzamide 4a-j with good yields up to 95% in mild reaction conditions. All the synthesized compounds were evaluated in vitro for anti-tubercular activity against the H37Rv strain of M. tuberculosis. Compounds 4c, 4d, and 4f were found active at 12.5 μg/mL against M. tb H37Rv. Electron withdrawing substituents present on aromatic side chains showed promising anti-tubercular activity.

System and network biology-based computational approaches for drug repositioning

Recent advances in computational biology have not only fastened the drug discovery process but have also proven to be a powerful tool for the search of existing molecules of therapeutic value for drug repurposing. The system biology-based drug repurposing approaches shorten the time and reduced the cost of the whole process when compared to de novo drug discovery. In the present pandemic situation, these computational approaches have emerged as a boon to tackle the COVID-19 associated morbidities and mortalities. In this chapter, we present the overview of system biology-based network system approaches which can be exploited for the drug repurposing of disease. Besides, we have included information on relevant repurposed drugs which are currently used for the treatment of COVID-19.

Chemsearch: collaborative compound libraries with structure-aware browsing

Summary

Chemsearch is a cross-platform server application for developing and managing a chemical compound library and associated data files, with an interface for browsing and search that allows for easy navigation to a compound of interest, similar compounds or compounds that have desired structural properties. With provisions for access control and centralized document and data storage, Chemsearch supports collaboration by distributed teams.

Availability and implementation

Chemsearch is a free and open-source Flask web application that can be linked to a Google Workspace account. Source code is available at https://github.com/gem-net/chemsearch (GPLv3 license). A Docker image allowing rapid deployment is available at https://hub.docker.com/r/cgemcci/chemsearch.

A random small molecule library screen identifies novel antagonists of the kinin receptor from the cattle fever tick, Rhipicephalus microplus (Acari: Ixodidae)

BACKGROUND

The southern cattle tick, Rhipicephalus microplus, is a primary vector of the deadly bovine disease babesiosis. Worldwide populations of ticks have developed resistance to acaricides, underscoring the need for novel target discovery for tick control. The arthropod-specific R. microplus kinin receptor is such a target, previously validated by silencing, which resulted in female reproductive fitness costs, including a reduced percentage of eggs hatching.

RESULTS

In order to identify potent small molecules that bind and activate or inhibit the kinin receptor, a high-throughput screening (HTS) assay was developed using a CHO-K1 cell line expressing the recombinant tick kinin receptor (BMLK₃ ). A total of ~20 000 molecules from a random in-house small molecule library were screened in a 'dual-addition' calcium fluorescence assay. This was followed by dose-response validation of the hit molecules identified both from HTS and an in silico screen of ~390 000 molecules. We validated 29 antagonists, 11 of them were full antagonists with IC₅₀ values between 0.67 and 8 μmol L^-1 . To explore the structure-activity relationships (SAR) of the small molecules, we tested the activities of seven analogs of the most potent identified antagonist, additionally discovering three full antagonists and four partial antagonists. These three potent antagonists (IC₅₀ < 3.2 μmol L^-1 ) were validated in vitro using the recombinant mosquito kinin receptor and showed similar antagonistic activities. In vivo, these three compounds also inhibited the mosquito hindgut contraction rate induced by a myotropic kinin agonist analog 1728.

CONCLUSION

Antagonists identified in this study could become pesticide leads and are reagents for probing the kinin signaling system. © 2021 Society of Chemical Industry.

Early Drug Development and Evaluation of Putative Antitubercular Compounds in the -Omics Era

Tuberculosis (TB) is an infectious disease caused by the bacterium Mycobacterium tuberculosis. According to the WHO, the disease is one of the top 10 causes of death of people worldwide. Mycobacterium tuberculosis is an intracellular pathogen with an unusually thick, waxy cell wall and a complex life cycle. These factors, combined with M. tuberculosis ability to enter prolonged periods of latency, make the bacterium very difficult to eradicate. The standard treatment of TB requires 6-20months, depending on the drug susceptibility of the infecting strain. The need to take cocktails of antibiotics to treat tuberculosis effectively and the emergence of drug-resistant strains prompts the need to search for new antitubercular compounds. This review provides a perspective on how modern -omic technologies facilitate the drug discovery process for tuberculosis treatment. We discuss how methods of DNA and RNA sequencing, proteomics, and genetic manipulation of organisms increase our understanding of mechanisms of action of antibiotics and allow the evaluation of drugs. We explore the utility of mathematical modeling and modern computational analysis for the drug discovery process. Finally, we summarize how -omic technologies contribute to our understanding of the emergence of drug resistance.

Pruned Machine Learning Models to Predict Aqueous Solubility

Solubility is a key metric for therapeutic compounds. Conversely, insoluble compounds cloud the accuracy of assays at all stages of chemical biology and drug discovery. Herein, we disclose naïve Bayesian classifier models to predict aqueous solubility. Publicly accessible aqueous solubility data were used to create two full, or nonpruned, training sets. These two sets were also combined to create a full fused set, and a training set comprised of a literature collation of solubility data was also considered as a reference. We tested different extents of data pruning on the training sets and constructed machine learning models that were evaluated with two independent, external test sets that contained compounds that were different from the training sets. The best pruned and fused model was significantly more accurate, in comparison to either the full model or the full fused model, with the prediction of these external test sets. By carefully removing data from the training set, less information can be used to create more accurate machine learning models for aqueous solubility. This knowledge and the curated training sets should prove useful to future machine learning approaches.

Machine Learning Algorithm Identifies an Antibiotic Vocabulary for Permeating Gram-Negative Bacteria

Drug discovery faces a crisis. The industry has used up the "obvious" space in which to find novel drugs for biomedical applications, and productivity is declining. One strategy to combat this is rational approaches to expand the search space without relying on chemical intuition, to avoid rediscovery of similar spaces. In this work, we present proof of concept of an approach to rationally identify a "chemical vocabulary" related to a specific drug activity of interest without employing known rules. We focus on the pressing concern of multidrug resistance in Pseudomonas aeruginosa by searching for submolecules that promote compound entry into this bacterium. By synergizing theory, computation, and experiment, we validate our approach, explain the molecular mechanism behind identified fragments promoting compound entry, and select candidate compounds from an external library that display good permeation ability.

Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace

The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.

Drug Discovery

Modern chemistry foundations were made in between the 18th and 19th centuries and have been extended in 20th century. R&D towards synthetic chemistry was introduced during the 1960s. Development of new molecular drugs from the herbal plants to synthetic chemistry is the fundamental scientific improvement. About 10-14 years are needed to develop a new molecule with an average cost of more than $800 million. Pharmaceutical industries spend the highest percentage of revenues, but the achievement of desired molecular entities into the market is not increasing proportionately. As a result, an approximate of 0.01% of new molecular entities are approved by the FDA. The highest failure rate is due to inadequate efficacy exhibited in Phase II of the drug discovery and development stage. Innovative technologies such as combinatorial chemistry, DNA sequencing, high-throughput screening, bioinformatics, computational drug design, and computer modeling are now utilized in the drug discovery. These technologies can accelerate the success rates in introducing new molecular entities into the market.

The benefits of in silico modeling to identify possible small-molecule drugs and their off-target interactions

The research into the use of small molecules as drugs continues to be a key driver in the development of molecular databases, computer-aided drug design software and collaborative platforms. The evolution of computational approaches is driven by the essential criteria that a drug molecule has to fulfill, from the affinity to targets to minimal side effects while having adequate absorption, distribution, metabolism, and excretion (ADME) properties. A combination of ligand- and structure-based drug development approaches is already used to obtain consensus predictions of small molecule activities and their off-target interactions. Further integration of these methods into easy-to-use workflows informed by systems biology could realize the full potential of available data in the drug discovery and reduce the attrition of drug candidates.

Data Mining and Computational Modeling of High-Throughput Screening Datasets

We are now seeing the benefit of investments made over the last decade in high-throughput screening (HTS) that is resulting in large structure activity datasets entering public and open databases such as ChEMBL and PubChem. The growth of academic HTS screening centers and the increasing move to academia for early stage drug discovery suggests a great need for the informatics tools and methods to mine such data and learn from it. Collaborative Drug Discovery, Inc. (CDD) has developed a number of tools for storing, mining, securely and selectively sharing, as well as learning from such HTS data. We present a new web based data mining and visualization module directly within the CDD Vault platform for high-throughput drug discovery data that makes use of a novel technology stack following modern reactive design principles. We also describe CDD Models within the CDD Vault platform that enables researchers to share models, share predictions from models, and create models from distributed, heterogeneous data. Our system is built on top of the Collaborative Drug Discovery Vault Activity and Registration data repository ecosystem which allows users to manipulate and visualize thousands of molecules in real time. This can be performed in any browser on any platform. In this chapter we present examples of its use with public datasets in CDD Vault. Such approaches can complement other cheminformatics tools, whether open source or commercial, in providing approaches for data mining and modeling of HTS data.

Development of a microbial dose response visualization and modelling application for QMRA modelers and educators

Microbial dose response modelling is vital to a well-characterized microbial risk estimate. Dose response modelling is an inherently multidisciplinary field, which collates knowledge and data from disparate scientific fields. This multidisciplinary nature presents a key challenge to the expansion of microbial dose response modelling into new groups of researchers and modelers. This research employs a dose response optimization R code used in 18 peer-reviewed research studies to develop a multi-functional dose response software. The underlying R code performs an optimization of the two primary dose response models using the MLE method and outputs statistical analyses of the fits and bootstrapped uncertainty information for the models. VizDR (Visual Dose Response) was developed to provide microbial dose response modelling capabilities to a larger audience. VizDR is programmed in JavaScript with underlying Python scripts for intercommunication with Rserve. VizDR allows for dose response model visualization and optimization of a user's own experimental data.

Machine learning and docking models for Mycobacterium tuberculosis topoisomerase I

There is a shortage of compounds that are directed towards new targets apart from those targeted by the FDA approved drugs used against Mycobacterium tuberculosis. Topoisomerase I (Mttopo I) is an essential mycobacterial enzyme and a promising target in this regard. However, it suffers from a shortage of known inhibitors. We have previously used computational approaches such as homology modeling and docking to propose 38 FDA approved drugs for testing and identified several active molecules. To follow on from this, we now describe the in vitro testing of a library of 639 compounds. These data were used to create machine learning models for Mttopo I which were further validated. The combined Mttopo I Bayesian model had a 5 fold cross validation receiver operator characteristic of 0.74 and sensitivity, specificity and concordance values above 0.76 and was used to select commercially available compounds for testing in vitro. The recently described crystal structure of Mttopo I was also compared with the previously described homology model and then used to dock the Mttopo I actives norclomipramine and imipramine. In summary, we describe our efforts to identify small molecule inhibitors of Mttopo I using a combination of machine learning modeling and docking studies in conjunction with screening of the selected molecules for enzyme inhibition. We demonstrate the experimental inhibition of Mttopo I by small molecule inhibitors and show that the enzyme can be readily targeted for lead molecule development.

Targeting Phenotypically Tolerant Mycobacterium tuberculosis

While the immune system is credited with averting tuberculosis in billions of individuals exposed to Mycobacterium tuberculosis, the immune system is also culpable for tempering the ability of antibiotics to deliver swift and durable cure of disease. In individuals afflicted with tuberculosis, host immunity produces diverse microenvironmental niches that support suboptimal growth, or complete growth arrest, of M. tuberculosis. The physiological state of nonreplication in bacteria is associated with phenotypic drug tolerance. Many of these host microenvironments, when modeled in vitro by carbon starvation, complete nutrient starvation, stationary phase, acidic pH, reactive nitrogen intermediates, hypoxia, biofilms, and withholding streptomycin from the streptomycin-addicted strain SS18b, render M. tuberculosis profoundly tolerant to many of the antibiotics that are given to tuberculosis patients in clinical settings. Targeting nonreplicating persisters is anticipated to reduce the duration of antibiotic treatment and rate of posttreatment relapse. Some promising drugs to treat tuberculosis, such as rifampin and bedaquiline, only kill nonreplicating M. tuberculosisin vitro at concentrations far greater than their minimal inhibitory concentrations against replicating bacilli. There is an urgent demand to identify which of the currently used antibiotics, and which of the molecules in academic and corporate screening collections, have potent bactericidal action on nonreplicating M. tuberculosis. With this goal, we review methods of high-throughput screening to target nonreplicating M. tuberculosis and methods to progress candidate molecules. A classification based on structures and putative targets of molecules that have been reported to kill nonreplicating M. tuberculosis revealed a rich diversity in pharmacophores.

Recent advancements in the development of anti-tuberculosis drugs

Modern chemotherapy has significantly improved patient outcomes against drug-sensitive tuberculosis. However, the rapid emergence of drug-resistant tuberculosis, together with the bacterium's ability to persist and remain latent present a major public health challenge. To overcome this problem, research into novel anti-tuberculosis targets and drug candidates is thus of paramount importance. This review article provides an overview of tuberculosis highlighting the recent advances and tools that are employed in the field of anti-tuberculosis drug discovery. The predominant focus is on anti-tuberculosis agents that are currently in the pipeline, i.e. clinical trials.

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.

Novel Cephalosporins Selectively Active on Nonreplicating Mycobacterium tuberculosis

We report two series of novel cephalosporins that are bactericidal to Mycobacterium tuberculosis alone of the pathogens tested, which only kill M. tuberculosis when its replication is halted by conditions resembling those believed to pertain in the host, and whose bactericidal activity is not dependent upon or enhanced by clavulanate, a β-lactamase inhibitor. The two classes of cephalosporins bear an ester or alternatively an oxadiazole isostere at C-2 of the cephalosporin ring system, a position that is almost exclusively a carboxylic acid in clinically used agents in the class. Representatives of the series kill M. tuberculosis within macrophages without toxicity to the macrophages or other mammalian cells.

Is preclinical data sharing the new norm?

Is preclinical data sharing the new norm? In my experience, it is certainly becoming more commonplace. However, it is not yet standard practice and remains the preserve of special projects. Here, I expound the benefits of sharing proprietary preclinical data using examples of successful initiatives. The main barriers to data sharing are then described, with suggestions for how these might be overcome. To maximise the benefits and minimise the risks involved, I suggest that organisations look to develop standard operating procedures for data sharing.

Fluorescent RNA Aptamers as a Tool to Study RNA-Modifying Enzymes

RNA-modifying enzymes are difficult to assay due to the absence of fluorometric substrates. Here we show that the Broccoli, a previously reported fluorescent RNA-dye complex, can be modified to contain N(6)-methyladenosine, a prevalent mRNA base modification. Methylated Broccoli is nonfluorescent but, upon demethylation by the RNA demethylases fat mass and obesity-associated protein (FTO) or ALKBH5, it binds and activates the fluorescence of its cognate fluorophore. We describe a high-throughput screen (HTS) for FTO inhibitors using the fluorogenic methylated Broccoli substrate HTS assay, which performs robustly with a Z' factor >0.8 in the LOPAC1280 library. This allowed the identification of novel high-affinity FTO inhibitors. Several of these compounds were selective for FTO over the related demethylase, ALKBH5, and increase methylation of endogenous FTO target mRNAs in cells. Lastly, we show that Broccoli can be modified to contain other base modifications, suggesting that this approach could be generally applicable for assaying diverse RNA-modifying enzymes.

Predicting Mouse Liver Microsomal Stability with "Pruned" Machine Learning Models and Public Data

PURPOSE

Mouse efficacy studies are a critical hurdle to advance translational research of potential therapeutic compounds for many diseases. Although mouse liver microsomal (MLM) stability studies are not a perfect surrogate for in vivo studies of metabolic clearance, they are the initial model system used to assess metabolic stability. Consequently, we explored the development of machine learning models that can enhance the probability of identifying compounds possessing MLM stability.

METHODS

Published assays on MLM half-life values were identified in PubChem, reformatted, and curated to create a training set with 894 unique small molecules. These data were used to construct machine learning models assessed with internal cross-validation, external tests with a published set of antitubercular compounds, and independent validation with an additional diverse set of 571 compounds (PubChem data on percent metabolism).

RESULTS

"Pruning" out the moderately unstable / moderately stable compounds from the training set produced models with superior predictive power. Bayesian models displayed the best predictive power for identifying compounds with a half-life ≥1 h.

CONCLUSIONS

Our results suggest the pruning strategy may be of general benefit to improve test set enrichment and provide machine learning models with enhanced predictive value for the MLM stability of small organic molecules. This study represents the most exhaustive study to date of using machine learning approaches with MLM data from public sources.

Prioritization of anti-malarial hits from nature: chemo-informatic profiling of natural products with in vitro antiplasmodial activities and currently registered anti-malarial drugs

BACKGROUND

A large number of natural products have shown in vitro antiplasmodial activities. Early identification and prioritization of these natural products with potential for novel mechanism of action, desirable pharmacokinetics and likelihood for development into drugs is advantageous. Chemo-informatic profiling of these natural products were conducted and compared to currently registered anti-malarial drugs (CRAD).

METHODS

Natural products with in vitro antiplasmodial activities (NAA) were compiled from various sources. These natural products were sub-divided into four groups based on inhibitory concentration (IC50). Key molecular descriptors and physicochemical properties were computed for these compounds and analysis of variance used to assess statistical significance amongst the sets of compounds. Molecular similarity analysis, estimation of drug-likeness, in silico pharmacokinetic profiling, and exploration of structure-activity landscape were also carried out on these sets of compounds.

RESULTS

A total of 1040 natural products were selected and a total of 13 molecular descriptors were analysed. Significant differences were observed among the sub-groups of NAA and CRAD for at least 11 of the molecular descriptors, including number of hydrogen bond donors and acceptors, molecular weight, polar and hydrophobic surface areas, chiral centres, oxygen and nitrogen atoms, and shape index. The remaining molecular descriptors, including clogP, number of rotatable bonds and number of aromatic rings, did not show any significant difference when comparing the two compound sets. Molecular similarity and chemical space analysis identified natural products that were structurally diverse from CRAD. Prediction of the pharmacokinetic properties and drug-likeness of these natural products identified over 50% with desirable drug-like properties. Nearly 70% of all natural products were identified as potentially promiscuous compounds. Structure-activity landscape analysis highlighted compound pairs that form 'activity cliffs'. In all, prioritization strategies for the NAA were proposed.

CONCLUSIONS

Chemo-informatic profiling of NAA and CRAD have produced a wealth of information that may guide decisions and facilitate anti-malarial drug development from natural products. Articulation of the information provided within an interactive data-mining environment led to a prioritized list of NAA.

Open Source Bayesian Models. 3. Composite Models for Prediction of Binned Responses

Bayesian models constructed from structure-derived fingerprints have been a popular and useful method for drug discovery research when applied to bioactivity measurements that can be effectively classified as active or inactive. The results can be used to rank candidate structures according to their probability of activity, and this ranking benefits from the high degree of interpretability when structure-based fingerprints are used, making the results chemically intuitive. Besides selecting an activity threshold, building a Bayesian model is fast and requires few or no parameters or user intervention. The method also does not suffer from such acute overtraining problems as quantitative structure–activity relationships or quantitative structure–property relationships (QSAR/QSPR). This makes it an approach highly suitable for automated workflows that are independent of user expertise or prior knowledge of the training data. We now describe a new method for creating a composite group of Bayesian models to extend the method to work with multiple states, rather than just binary. Incoming activities are divided into bins, each covering a mutually exclusive range of activities. For each of these bins, a Bayesian model is created to model whether or not the compound belongs in the bin. Analyzing putative molecules using the composite model involves making a prediction for each bin and examining the relative likelihood for each assignment, for example, highest value wins. The method has been evaluated on a collection of hundreds of data sets extracted from ChEMBL v20 and validated data sets for ADME/Tox and bioactivity.

Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery

Integrated computational approaches for Mycobacterium tuberculosis (Mtb) are useful to identify new molecules that could lead to future tuberculosis (TB) drugs. Our approach uses information derived from the TBCyc pathway and genome database, the Collaborative Drug Discovery TB database combined with 3D pharmacophores and dual event Bayesian models of whole-cell activity and lack of cytotoxicity. We have prioritized a large number of molecules that may act as mimics of substrates and metabolites in the TB metabolome. We computationally searched over 200,000 commercial molecules using 66 pharmacophores based on substrates and metabolites from Mtb and further filtering with Bayesian models. We ultimately tested 110 compounds in vitro that resulted in two compounds of interest, BAS 04912643 and BAS 00623753 (MIC of 2.5 and 5 μg/mL, respectively). These molecules were used as a starting point for hit-to-lead optimization. The most promising class proved to be the quinoxaline di-N-oxides, evidenced by transcriptional profiling to induce mRNA level perturbations most closely resembling known protonophores. One of these, SRI58 exhibited an MIC = 1.25 μg/mL versus Mtb and a CC₅₀ in Vero cells of >40 μg/mL, while featuring fair Caco-2 A-B permeability (2.3 x 10⁻⁶ cm/s), kinetic solubility (125 μM at pH 7.4 in PBS) and mouse metabolic stability (63.6% remaining after 1 h incubation with mouse liver microsomes). Despite demonstration of how a combined bioinformatics/cheminformatics approach afforded a small molecule with promising in vitro profiles, we found that SRI58 did not exhibit quantifiable blood levels in mice.

Application of data science tools to quantify and distinguish between structures and models in molecular dynamics datasets

Structure quantification is key to successful mining and extraction of core materials knowledge from both multiscale simulations as well as multiscale experiments. The main challenge stems from the need to transform the inherently high dimensional representations demanded by the rich hierarchical material structure into useful, high value, low dimensional representations. In this paper, we develop and demonstrate the merits of a data-driven approach for addressing this challenge at the atomic scale. The approach presented here is built on prior successes demonstrated for mesoscale representations of material internal structure, and involves three main steps: (i) digital representation of the material structure, (ii) extraction of a comprehensive set of structure measures using the framework of n-point spatial correlations, and (iii) identification of data-driven low dimensional measures using principal component analyses. These novel protocols, applied on an ensemble of structure datasets output from molecular dynamics (MD) simulations, have successfully classified the datasets based on several model input parameters such as the interatomic potential and the temperature used in the MD simulations.

Rapid, Semiquantitative Assay To Discriminate among Compounds with Activity against Replicating or Nonreplicating Mycobacterium tuberculosis

The search for drugs that can kill replicating and nonreplicating Mycobacterium tuberculosis faces practical bottlenecks. Measurement of CFU and discrimination of bacteriostatic from bactericidal activity are costly in compounds, supplies, labor, and time. Testing compounds against M. tuberculosis under conditions that prevent the replication of M. tuberculosis often involves a second phase of the test in which conditions are altered to permit the replication of bacteria that survived the first phase. False-positive determinations of activity against nonreplicating M. tuberculosis may arise from carryover of compounds from the nonreplicating stage of the assay that act in the replicating stage. We mitigate these problems by carrying out a 96-well microplate liquid MIC assay and then transferring an aliquot of each well to a second set of plates in which each well contains agar supplemented with activated charcoal. After 7 to 10 days—about 2 weeks sooner than required to count CFU—fluorometry reveals whether M. tuberculosis bacilli in each well have replicated extensively enough to reduce a resazurin dye added for the final hour. This charcoal agar resazurin assay (CARA) distinguishes between bacterial biomasses in any two wells that differ by 2 to 3 log₁₀ CFU. The CARA thus serves as a pretest and semiquantitative surrogate for longer, more laborious, and expensive CFU-based assays, helps distinguish bactericidal from bacteriostatic activity, and identifies compounds that are active under replicating conditions, nonreplicating conditions, or both. Results for 14 antimycobacterial compounds, including tuberculosis (TB) drugs, revealed that PA-824 (pretomanid) and TMC207 (bedaquiline) are largely bacteriostatic.

Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets

On the order of hundreds of absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) models have been described in the literature in the past decade which are more often than not inaccessible to anyone but their authors. Public accessibility is also an issue with computational models for bioactivity, and the ability to share such models still remains a major challenge limiting drug discovery. We describe the creation of a reference implementation of a Bayesian model-building software module, which we have released as an open source component that is now included in the Chemistry Development Kit (CDK) project, as well as implemented in the CDD Vault and in several mobile apps. We use this implementation to build an array of Bayesian models for ADME/Tox, in vitro and in vivo bioactivity, and other physicochemical properties. We show that these models possess cross-validation receiver operator curve values comparable to those generated previously in prior publications using alternative tools. We have now described how the implementation of Bayesian models with FCFP6 descriptors generated in the CDD Vault enables the rapid production of robust machine learning models from public data or the user’s own datasets. The current study sets the stage for generating models in proprietary software (such as CDD) and exporting these models in a format that could be run in open source software using CDK components. This work also demonstrates that we can enable biocomputation across distributed private or public datasets to enhance drug discovery.

A brief review of recent Charcot-Marie-Tooth research and priorities

This brief review of current research progress on Charcot-Marie-Tooth (CMT) disease is a summary of discussions initiated at the Hereditary Neuropathy Foundation (HNF) scientific advisory board meeting on November 7, 2014. It covers recent published and unpublished in vitro and in vivo research. We discuss recent promising preclinical work for CMT1A, the development of new biomarkers, the characterization of different animal models, and the analysis of the frequency of gene mutations in patients with CMT. We also describe how progress in related fields may benefit CMT therapeutic development, including the potential of gene therapy and stem cell research. We also discuss the potential to assess and improve the quality of life of CMT patients. This summary of CMT research identifies some of the gaps which may have an impact on upcoming clinical trials. We provide some priorities for CMT research and areas which HNF can support. The goal of this review is to inform the scientific community about ongoing research and to avoid unnecessary overlap, while also highlighting areas ripe for further investigation. The general collaborative approach we have taken may be useful for other rare neurological diseases.

Small molecules with antiviral activity against the Ebola virus

The recent outbreak of the Ebola virus in West Africa has highlighted the clear shortage of broad-spectrum antiviral drugs for emerging viruses. There are numerous FDA approved drugs and other small molecules described in the literature that could be further evaluated for their potential as antiviral compounds. These molecules are in addition to the few new antivirals that have been tested in Ebola patients but were not originally developed against the Ebola virus, and may play an important role as we await an effective vaccine. The balance between using FDA approved drugs versus novel antivirals with minimal safety and no efficacy data in humans should be considered. We have evaluated 55 molecules from the perspective of an experienced medicinal chemist as well as using simple molecular properties and have highlighted 16 compounds that have desirable qualities as well as those that may be less desirable. In addition we propose that a collaborative database for sharing such published and novel information on small molecules is needed for the research community studying the Ebola virus.

Parallel worlds of public and commercial bioactive chemistry data

The availability of structures and linked bioactivity data in databases is powerfully enabling for drug discovery and chemical biology. However, we now review some confounding issues with the divergent expansions of public and commercial sources of chemical structures. These are associated with not only expanding patent extraction but also increasingly large vendor collections amassed via different selection criteria between SciFinder from Chemical Abstracts Service (CAS) and major public sources such as PubChem, ChemSpider, UniChem, and others. These increasingly massive collections may include both real and virtual compounds, as well as so-called prophetic compounds from patents. We address a range of issues raised by the challenges faced resolving the NIH probe compounds. In addition we highlight the confounding of prior-art searching by virtual compounds that could impact the composition of matter patentability of a new medicinal chemistry lead. Finally, we propose some potential solutions.

Collaboration for rare disease drug discovery research

Rare disease research has reached a tipping point, with the confluence of scientific and technologic developments that if appropriately harnessed, could lead to key breakthroughs and treatments for this set of devastating disorders. Industry-wide trends have revealed that the traditional drug discovery research and development (R&D) model is no longer viable, and drug companies are evolving their approach. Rather than only pursue blockbuster therapeutics for heterogeneous, common diseases, drug companies have increasingly begun to shift their focus to rare diseases. In academia, advances in genetics analyses and disease mechanisms have allowed scientific understanding to mature, but the lack of funding and translational capability severely limits the rare disease research that leads to clinical trials. Simultaneously, there is a movement towards increased research collaboration, more data sharing, and heightened engagement and active involvement by patients, advocates, and foundations. The growth in networks and social networking tools presents an opportunity to help reach other patients but also find researchers and build collaborations. The growth of collaborative software that can enable researchers to share their data could also enable rare disease patients and foundations to manage their portfolio of funded projects for developing new therapeutics and suggest drug repurposing opportunities. Still there are many thousands of diseases without treatments and with only fragmented research efforts. We will describe some recent progress in several rare diseases used as examples and propose how collaborations could be facilitated. We propose that the development of a center of excellence that integrates and shares informatics resources for rare diseases sponsored by all of the stakeholders would help foster these initiatives.

Databases and collaboration require standards for human stem cell research

Stem cell research is at an important juncture: despite significant potential for human health and several countries with key initiatives to expedite commercialization, there are gaps in capturing and exploiting the results of past and current research. Here, we propose a concerted plan that could be taken to foster a more collaborative approach and ensure that all research efforts can be leveraged across the community. The creation of a definitive centralized database repository, or at least harmonized data repositories, for stem cell groups in academia and industry, enabling secure selective sharing of data when needed, could provide the core structure that is sought globally and protect intellectual property. The development of minimum information about stem cell experiments (MIASCE) could be key to this development.

Diversity-oriented synthesis probe targets Plasmodium falciparum cytochrome b ubiquinone reduction site and synergizes with oxidation site inhibitors

Background. The emergence and spread of drug resistance to current antimalarial therapies remains a pressing concern, escalating the need for compounds that demonstrate novel modes of action. Diversity-Oriented Synthesis (DOS) libraries bridge the gap between conventional small molecule and natural product libraries, allowing the interrogation of more diverse chemical space in efforts to identify probes of novel parasite pathways.

Methods. We screened and optimized a probe from a DOS library using whole-cell phenotypic assays. Resistance selection and whole-genome sequencing approaches were employed to identify the cellular target of the compounds.

Results. We identified a novel macrocyclic inhibitor of Plasmodium falciparum with nanomolar potency and identified the reduction site of cytochrome b as its cellular target. Combination experiments with reduction and oxidation site inhibitors showed synergistic inhibition of the parasite.

Conclusions. The cytochrome b oxidation center is a validated antimalarial target. We show that the reduction site of cytochrome b is also a druggable target. Our results demonstrating a synergistic relationship between oxidation and reduction site inhibitors suggests a future strategy for new combination therapies in the treatment of malaria.

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.

Structural bioinformatics of the interactome

The past decade has seen a dramatic expansion in the number and range of techniques available to obtain genome-wide information and to analyze this information so as to infer both the functions of individual molecules and how they interact to modulate the behavior of biological systems. Here, we review these techniques, focusing on the construction of physical protein-protein interaction networks, and highlighting approaches that incorporate protein structure, which is becoming an increasingly important component of systems-level computational techniques. We also discuss how network analyses are being applied to enhance our basic understanding of biological systems and their disregulation, as well as how these networks are being used in drug development.

New target prediction and visualization tools incorporating open source molecular fingerprints for TB Mobile 2.0

BACKGROUND

We recently developed a freely available mobile app (TB Mobile) for both iOS and Android platforms that displays Mycobacterium tuberculosis (Mtb) active molecule structures and their targets with links to associated data. The app was developed to make target information available to as large an audience as possible.

RESULTS

We now report a major update of the iOS version of the app. This includes enhancements that use an implementation of ECFP_6 fingerprints that we have made open source. Using these fingerprints, the user can propose compounds with possible anti-TB activity, and view the compounds within a cluster landscape. Proposed compounds can also be compared to existing target data, using a näive Bayesian scoring system to rank probable targets. We have curated an additional 60 new compounds and their targets for Mtb and added these to the original set of 745 compounds. We have also curated 20 further compounds (many without targets in TB Mobile) to evaluate this version of the app with 805 compounds and associated targets.

CONCLUSIONS

TB Mobile can now manage a small collection of compounds that can be imported from external sources, or exported by various means such as email or app-to-app inter-process communication. This means that TB Mobile can be used as a node within a growing ecosystem of mobile apps for cheminformatics. It can also cluster compounds and use internal algorithms to help identify potential targets based on molecular similarity. TB Mobile represents a valuable dataset, data-visualization aid and target prediction tool.

Are bigger data sets better for machine learning? Fusing single-point and dual-event dose response data for Mycobacterium tuberculosis

Tuberculosis is a major, neglected disease for which the quest to find new treatments continues. There is an abundance of data from large phenotypic screens in the public domain against Mycobacterium tuberculosis (Mtb). Since machine learning methods can learn from past data, we were interested in addressing whether more data builds better models. We now describe using Bayesian machine learning to assess whether we can improve our models by combining the large quantities of single-point data with the much smaller (higher quality) dual-event data sets, which use both dose-response data for both whole-cell antitubercular activity and Vero cell cytotoxicity. We have evaluated 12 models ranging from different single-point, dual-event dose-response, single-point and dual-event dose-response as well as combined data sets for three distinct data sets from the same laboratory. We used a fourth data set of active and inactive compounds from the same group as well as a smaller set of 177 active compounds from GlaxoSmithKline as test sets. Our data suggest combining single-point with dual-event dose-response data does not diminish the internal or external predictive ability of the models based on the receiver operator curve (ROC) for these models (internal ROC range 0.83-0.91, external ROC range 0.62-0.83) compared to the orders of magnitude smaller dual-event models (internal ROC range 0.6-0.83 and external ROC 0.54-0.83). In conclusion, models developed with 1200-5000 compounds appear to be as predictive as those generated with 25 000-350 000 molecules. Our results have implications for justifying further high-throughput screening versus focused testing based on model predictions.

Crowdsourcing medical expertise in near real time

Given the pace of discovery in medicine, accessing the literature to make informed decisions at the point of care has become increasingly difficult. Although the Internet creates unprecedented access to information, gaps in the medical literature and inefficient searches often leave healthcare providers' questions unanswered. Advances in social computation and human computer interactions offer a potential solution to this problem. We developed and piloted the mobile application DocCHIRP, which uses a system of point-to-multipoint push notifications designed to help providers problem solve by crowdsourcing from their peers. Over the 244-day pilot period, 85 registered users logged 1544 page views and sent 45 consult questions. The median initial first response from the crowd occurred within 19 minutes. Review of the transcripts revealed several dominant themes, including complex medical decision making and inquiries related to prescription medication use. Feedback from the post-trial survey identified potential hurdles related to medical crowdsourcing, including a reluctance to expose personal knowledge gaps and the potential risk for "distracted doctoring." Users also suggested program modifications that could support future adoption, including changes to the mobile interface and mechanisms that could expand the crowd of participating healthcare providers.

Evolving BioAssay Ontology (BAO): modularization, integration and applications

The lack of established standards to describe and annotate biological assays and screening outcomes in the domain of drug and chemical probe discovery is a severe limitation to utilize public and proprietary drug screening data to their maximum potential. We have created the BioAssay Ontology (BAO) project (http://bioassayontology.org) to develop common reference metadata terms and definitions required for describing relevant information of low-and high-throughput drug and probe screening assays and results. The main objectives of BAO are to enable effective integration, aggregation, retrieval, and analyses of drug screening data. Since we first released BAO on the BioPortal in 2010 we have considerably expanded and enhanced BAO and we have applied the ontology in several internal and external collaborative projects, for example the BioAssay Research Database (BARD). We describe the evolution of BAO with a design that enables modeling complex assays including profile and panel assays such as those in the Library of Integrated Network-based Cellular Signatures (LINCS). One of the critical questions in evolving BAO is the following: how can we provide a way to efficiently reuse and share among various research projects specific parts of our ontologies without violating the integrity of the ontology and without creating redundancies. This paper provides a comprehensive answer to this question with a description of a methodology for ontology modularization using a layered architecture. Our modularization approach defines several distinct BAO components and separates internal from external modules and domain-level from structural components. This approach facilitates the generation/extraction of derived ontologies (or perspectives) that can suit particular use cases or software applications. We describe the evolution of BAO related to its formal structures, engineering approaches, and content to enable modeling of complex assays and integration with other ontologies and datasets.