Barry A. Bunin

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

A convergence of different commercial and publicly accessible chemical informatics, databases and social networking tools is positioned to change the way that research collaborations are initiated, maintained and expanded, particularly in the realm of neglected diseases. A community-based platform that combines traditional drug discovery informatics with Web2.0 features in secure groups is believed to be the key to facilitating richer, instantaneous collaborations involving sensitive drug discovery data and intellectual property. Heterogeneous chemical and biological data from low-throughput or high-throughput experiments are archived, mined and then selectively shared either just securely between specifically designated colleagues or openly on the Internet in standardized formats. We will illustrate several case studies for anti-malarial research enabled by this platform, which we suggest could be easily expanded more broadly for pharmaceutical research in general.

Using Open Source Computational Tools for Predicting Human Metabolic Stability and Additional Absorption, Distribution, Metabolism, Excretion, and Toxicity Properties

Ligand-based computational models could be more readily shared between researchers and organizations if they were generated with open source molecular descriptors [e.g., chemistry development kit (CDK)] and modeling algorithms, because this would negate the requirement for proprietary commercial software. We initially evaluated open source descriptors and model building algorithms using a training set of approximately 50,000 molecules and a test set of approximately 25,000 molecules with human liver microsomal metabolic stability data. A C5.0 decision tree model demonstrated that CDK descriptors together with a set of Smiles Arbitrary Target Specification (SMARTS) keys had good statistics [κ = 0.43, sensitivity = 0.57, specificity = 0.91, and positive predicted value (PPV) = 0.64], equivalent to those of models built with commercial Molecular Operating Environment 2D (MOE2D) and the same set of SMARTS keys (κ = 0.43, sensitivity = 0.58, specificity = 0.91, and PPV = 0.63). Extending the dataset to ∼193,000 molecules and generating a continuous model using Cubist with a combination of CDK and SMARTS keys or MOE2D and SMARTS keys confirmed this observation. When the continuous predictions and actual values were binned to get a categorical score we observed a similar κ statistic (0.42). The same combination of descriptor set and modeling method was applied to passive permeability and P-glycoprotein efflux data with similar model testing statistics. In summary, open source tools demonstrated predictive results comparable to those of commercial software with attendant cost savings. We discuss the advantages and disadvantages of open source descriptors and the opportunity for their use as a tool for organizations to share data precompetitively, avoiding repetition and assisting drug discovery.

Enhancing Hit Identification in Mycobacterium tuberculosis Drug Discovery Using Validated Dual-Event Bayesian Models

High-throughput screening (HTS) in whole cells is widely pursued to find compounds active against Mycobacterium tuberculosis (Mtb) for further development towards new tuberculosis (TB) drugs. Hit rates from these screens, usually conducted at 10 to 25 µM concentrations, typically range from less than 1% to the low single digits. New approaches to increase the efficiency of hit identification are urgently needed to learn from past screening data. The pharmaceutical industry has for many years taken advantage of computational approaches to optimize compound libraries for in vitro testing, a practice not fully embraced by academic laboratories in the search for new TB drugs. Adapting these proven approaches, we have recently built and validated Bayesian machine learning models for predicting compounds with activity against Mtb based on publicly available large-scale HTS data from the Tuberculosis Antimicrobial Acquisition Coordinating Facility. We now demonstrate the largest prospective validation to date in which we computationally screened 82,403 molecules with these Bayesian models, assayed a total of 550 molecules in vitro, and identified 124 actives against Mtb. Individual hit rates for the different datasets varied from 15–28%. We have identified several FDA approved and late stage clinical candidate kinase inhibitors with activity against Mtb which may represent starting points for further optimization. The computational models developed herein and the commercially available molecules derived from them are now available to any group pursuing Mtb drug discovery.

Chemical space: missing pieces in cheminformatics.

ABSTRACTCheminformatics is at a turning point, the pharmaceutical industry benefits from using the various methods developed over the last twenty years, but in our opinion we need to see greater development of novel approaches that non-experts can use. This will be achieved by more collaborations between software companies, academics and the evolving pharmaceutical industry. We suggest that cheminformatics should also be looking to other industries that use high performance computing technologies for inspiration. We describe the needs and opportunities which may benefit from the development of open cheminformatics technologies, mobile computing, the movement of software to the cloud and precompetitive initiatives.

Computational prediction and validation of an expert's evaluation of chemical probes.

In a decade with over half a billion dollars of investment, more than 300 chemical probes have been identified to have biological activity through NIH funded screening efforts. We have collected the evaluations of an experienced medicinal chemist on the likely chemistry quality of these probes based on a number of criteria including literature related to the probe and potential chemical reactivity. Over 20% of these probes were found to be undesirable. Analysis of the molecular properties of these compounds scored as desirable suggested higher pKa, molecular weight, heavy atom count, and rotatable bond number. We were particularly interested whether the human evaluation aspect of medicinal chemistry due diligence could be computationally predicted. We used a process of sequential Bayesian model building and iterative testing as we included additional probes. Following external validation of these methods and comparing different machine learning methods, we identified Bayesian models with accuracy comparable to other measures of drug-likeness and filtering rules created to date.

Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery

Background Chagas disease is a neglected tropical disease (NTD) caused by the eukaryotic parasite Trypanosoma cruzi. The current clinical and preclinical pipeline for T. cruzi is extremely sparse and lacks drug target diversity. Methodology/Principal Findings In the present study we developed a computational approach that utilized data from several public whole-cell, phenotypic high throughput screens that have been completed for T. cruzi by the Broad Institute, including a single screen of over 300,000 molecules in the search for chemical probes as part of the NIH Molecular Libraries program. We have also compiled and curated relevant biological and chemical compound screening data including (i) compounds and biological activity data from the literature, (ii) high throughput screening datasets, and (iii) predicted metabolites of T. cruzi metabolic pathways. This information was used to help us identify compounds and their potential targets. We have constructed a Pathway Genome Data Base for T. cruzi. In addition, we have developed Bayesian machine learning models that were used to virtually screen libraries of compounds. Ninety-seven compounds were selected for in vitro testing, and 11 of these were found to have EC50 < 10μM. We progressed five compounds to an in vivo mouse efficacy model of Chagas disease and validated that the machine learning model could identify in vitro active compounds not in the training set, as well as known positive controls. The antimalarial pyronaridine possessed 85.2% efficacy in the acute Chagas mouse model. We have also proposed potential targets (for future verification) for this compound based on structural similarity to known compounds with targets in T. cruzi. Conclusions/ Significance We have demonstrated how combining chemoinformatics and bioinformatics for T. cruzi drug discovery can bring interesting in vivo active molecules to light that may have been overlooked. The approach we have taken is broadly applicable to other NTDs.

Alternative business models for drug discovery

A fundamental problem The challenge, cost, and risk of modern drug discovery are well documented [1]. Today, anyone with a new idea for a drug must raise capital (often >

Fast and accurate semantic annotation of bioassays exploiting a hybrid of machine learning and user confirmation

100M) to take the idea through phase 2 trials to obtain a strong return on investment (ROI). Buyers of a drug asset must pay a premium (often >

Thermodynamic Proxies to Compensate for Biases in Drug Discovery Methods

1B) after human efficacy and safety are demonstrated. Developers of a new pharmaceutical technology know first hand how hard it is in the biotech space to obtain a ROI

Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB).

Bioinformatics and computer aided drug design rely on the curation of a large number of protocols for biological assays that measure the ability of potential drugs to achieve a therapeutic effect. These assay protocols are generally published by scientists in the form of plain text, which needs to be more precisely annotated in order to be useful to software methods. We have developed a pragmatic approach to describing assays according to the semantic definitions of the BioAssay Ontology (BAO) project, using a hybrid of machine learning based on natural language processing, and a simplified user interface designed to help scientists curate their data with minimum effort. We have carried out this work based on the premise that pure machine learning is insufficiently accurate, and that expecting scientists to find the time to annotate their protocols manually is unrealistic. By combining these approaches, we have created an effective prototype for which annotation of bioassay text within the domain of the training set can be accomplished very quickly. Well-trained annotations require single-click user approval, while annotations from outside the training set domain can be identified using the search feature of a well-designed user interface, and subsequently used to improve the underlying models. By drastically reducing the time required for scientists to annotate their assays, we can realistically advocate for semantic annotation to become a standard part of the publication process. Once even a small proportion of the public body of bioassay data is marked up, bioinformatics researchers can begin to construct sophisticated and useful searching and analysis algorithms that will provide a diverse and powerful set of tools for drug discovery researchers.