Computational Prediction and Validation of an Expert’s Evaluation of Chemical Probes

Litterman, Nadia K.; Lipinski, Christopher A.; Bunin, Barry A.; Ekins, Sean

doi:10.1021/ci500445u

Cited by 27 publications

(32 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This strategy is based on our and others’ extensive use of this method to build predictive models for ADME/Tox properties [46,59,64,92,93] as well as models for activity against Mycobacterium tuberculosis ( Mtb ) [1,55,69,73,94]. For means of comparison, we also tested our datasets with Support Vector Machine and RP-Random Forest models.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2015

Self Cite

View full text Add to dashboard Cite

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 hour. 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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

et al. 2015

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the process of this work we created a drop-in replacement for the widely used ECFP6 and FCFP6 fingerprints [80] and made the resulting code available to the public as a feature in the Chemical Development Kit (CDK) project under an open source license. These ‘CDD models’ have been applied to several innovative areas including modeling decision making for chemical probes [81] as well as developed ADMET models that leverage publically accessible data from industry and academia [82]. The open source descriptors and Bayesian algorithm have also been used outside of the CDD Vault to create several thousand models with the ChEMBL data, possibly representing the future of using thousands of models to score compounds simultaneously [83].…”

Section: Machine Learning Models For M Tuberculosismentioning

confidence: 99%

Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB)

Ekins

Spektor

Clark

et al. 2017

Drug Discovery Today

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…These include FDA approved drugs and compounds that have been identified by in vitro screening for repurposing 74 , and the National Center for Advancing Translational Sciences (NCATS) molecules for repurposing 75 . CDD has recently added the NIH's Molecular Libraries Probe Production Centers Network (MLPCN) probe compounds alongside the scoring of these molecules by an experienced medicinal chemist 76 . Comparison of these public datasets with private data may lead to novel drug repositioning ideas, which may in turn mean an accelerated path towards new treatments 74 , 77 , 78 .…”

Section: Software For Collaborationsmentioning

confidence: 99%

Collaboration for rare disease drug discovery research

et al. 2014

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Computational Prediction and Validation of an Expert’s Evaluation of Chemical Probes

Cited by 27 publications

References 46 publications

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

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

Collaborative drug discovery for More Medicines for Tuberculosis (MM4TB)

Collaboration for rare disease drug discovery research

Contact Info

Product

Resources

About