Ekins S.,Collaborative Drug Discovery, Inc. |
Clark A.M.,Molecular Materials Informatics Inc. |
Sarker M.,SRI International
Journal of Cheminformatics | Year: 2013
Background: An increasing number of researchers are focused on strategies for developing inhibitors of Mycobacterium tuberculosis (Mtb) as tuberculosis (TB) drugs. Results: In order to learn from prior work we have collated information on molecules screened versus Mtb and their targets which has been made available in the Collaborative Drug Discovery (CDD) database. This dataset contains published data on target, essentiality, links to PubMed, TBDB, TBCyc (which provides a pathway-based visualization of the entire cellular biochemical network) and human homolog information. The development of mobile cheminformatics apps could lower the barrier to drug discovery and promote collaboration. Therefore we have used this set of over 700 molecules screened versus Mtb and their targets to create a free mobile app (TB Mobile) that displays molecule structures and links to the bioinformatics data. By input of a molecular structures and performing a similarity search within the app we can infer potential targets or search by targets to retrieve compounds known to be active. Conclusions: TB Mobile may assist researchers as part of their workflow in identifying potential targets for hits generated from phenotypic screening and in prioritizing them for further follow-up. The app is designed to lower the barriers to accessing this information, so that all researchers with an interest in combatting this deadly disease can use it freely to the benefit of their own efforts. © 2013 Ekins et al.; licensee Chemistry Central Ltd. Source
Ekins S.,Collaborative Drug Discovery, Inc.
Tuberculosis | Year: 2014
Alternatives to small molecule or vaccine approaches to treating tuberculosis are rarely discussed. Attacking Mycobacterium tuberculosis in the granuloma represents a challenge. It is proposed that the conjugation of small molecules onto a monoclonal antibody that recognizes macrophage or lymphocytes cell surface receptors, might be a way to target the bacteria in the granuloma. This antibody drug conjugate approach is currently being used in 2 FDA approved targeted cancer therapies. The pros and cons of this proposal for further research are discussed. © 2014 Elsevier Ltd. Source
Ekins S.,Collaborative Drug Discovery, Inc. |
Freundlich J.S.,Rutgers University |
Reynolds R.C.,University of Alabama at Birmingham
Journal of Chemical Information and Modeling | Year: 2013
The search for new tuberculosis treatments continues as we need to find molecules that can act more quickly, be accommodated in multidrug regimens, and overcome ever increasing levels of drug resistance. Multiple large scale phenotypic high-throughput screens against Mycobacterium tuberculosis (Mtb) have generated dose response data, enabling the generation of machine learning models. These models also incorporated cytotoxicity data and were recently validated with a large external data set. A cheminformatics data-fusion approach followed by Bayesian machine learning, Support Vector Machine, or Recursive Partitioning model development (based on publicly available Mtb screening data) was used to compare individual data sets and subsequent combined models. A set of 1924 commercially available molecules with promising antitubercular activity (and lack of relative cytotoxicity to Vero cells) were used to evaluate the predictive nature of the models. We demonstrate that combining three data sets incorporating antitubercular and cytotoxicity data in Vero cells from our previous screens results in external validation receiver operator curve (ROC) of 0.83 (Bayesian or RP Forest). Models that do not have the highest 5-fold cross-validation ROC scores can outperform other models in a test set dependent manner. We demonstrate with predictions for a recently published set of Mtb leads from GlaxoSmithKline that no single machine learning model may be enough to identify compounds of interest. Data set fusion represents a further useful strategy for machine learning construction as illustrated with Mtb. Coverage of chemistry and Mtb target spaces may also be limiting factors for the whole-cell screening data generated to date. © 2013 American Chemical Society. Source
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 150.00K | Year: 2011
DESCRIPTION (provided by applicant): Collaborative Drug Discovery, Inc. (CDD) proposes to create a novel web-based software platform that enables scientists to work together effectively to discover and improve new drug leads, yet with the option not to reveal chemical structures to each other. It will create the first practical system of biocomputational analysis across distributed datasets with different owners, while respecting data privacy. By lowering this key barrier to collaboration, the platform willaccelerate the pre-clinical drug discovery pipeline. Research aimed at neglected diseases and orphan indications will especially benefit, because they often rely on the loosely affiliated efforts of academic investigators, non-profit foundations, government laboratories, and small biotechnology firms ( extra-pharma entities). Such efforts typically lack not only the resources but also the integrated workflows of discovery projects conducted at large pharmaceutical companies (within which data can be shared freely across departments). The project will for the first time enable researchers focused on neglected diseases and orphan indications to effectively exploit biocomputational tools such as virtual screening and ADME/Tox predictions, which are now considered to be standard and indispensible components of early discovery workflows within large pharma. It will also make it easier for these extra-pharma researchers to collaborate with large pharma and benefit from large pharma's significant investment accumulating large high-quality datasets. In Phase 1 of the proposed SBIR, CDD will leverage ongoing collaborations to prove the feasibility and value of the approach with prospective potency predictions in advance of experimental confirmation. Key collaborators include Prof. Carl Nathan at Weill Cornell Medical College, Dr. Clifton Barry, III, at NIAID, and Allen Casey at the Infectious Disease Research Institute (IDRI). Their groups will serve as an experimental test bed for the project. They all have ongoingscreening programs to discover compounds active against tuberculosis (TB). Specific aims for Phase 1 include: 1. Demonstrate the value to the collaborating screening centers of creating computational TB screening models derived from distributed, heterogeneous collections of data and exploiting the models prospectively to filter and prioritize the molecules scheduled to be screened. Validate the hypothesis that by selecting subsets enriched with active compounds, the centers can efficiently explore more ofchemical space than would otherwise be possible with limited resources. 2. Develop initial standards for specifying models (including purpose, inputs, outputs, algorithms, descriptor types, domain of applicability and other parameters necessary for presentation, interpretation, and exchange) that will form the outline for more comprehensive software prototypes that CDD will iteratively develop, deploy, test and validate in Phase 2. PUBLIC HEALTH RELEVANCE: The proposed project will create novel computational tools that will help researchers to accelerate the discovery of new and improved drugs against a wide range of diseases. These tools will particularly benefit researchers working on diseases that leading pharmaceutical companies have largely ignored because they are not perceived as highly profitable opportunities, despite the fact that in many cases they afflict millions of people.
Collaborative Drug Discovery, Inc. | Date: 2015-07-07
A process for identifying, accurately selecting, and storing scientific data that is present in textual formats. The process includes providing scientific data located in a text document and searching the text document using a computer and selecting a plurality of key words and phrases using an algorithm. The selected key words and phrases are matched with a plurality of semantic definitions and a plurality of semantic definition-key words and phrase pairs are created. The created plurality of semantic definition-key words and phrase pairs are displayed to a user via a computer user interface and the user selects which of the created plurality of semantic definition-key words and phrase pairs are accurate. The process also includes storing the selected and accurate semantic definition-key words and phrase pairs in computer memory.