Our goal is to bring multiple therapeutics to the clinic for rare and neglected diseases using machine learning and our extensive drug discovery expertise. Collaborations Pharmaceuticals, Inc. is a privately owned company that performs research and development on innovative therapeutics for multiple rare and infectious diseases.
We partner with academics and companies to identify and translate early preclinical to clinical stage assets. Assay Central is one of our machine learning platforms to create and build our drug pipeline. MegaTox, MegaTrans and MegaPredict are collections of machine learning models that can be used to predict molecular properties and bioactivities that could help in your projects.
We partner with academics and companies to identify and translate early preclinical to clinical stage assets. Assay Central is one of our machine learning platforms to create and build our drug pipeline. MegaTox, MegaTrans and MegaPredict are collections of machine learning models that can be used to predict molecular properties and bioactivities that could help in your projects.
Services
We provide decades of drug discovery experience and our computational approaches to design and score new compounds versus targets, as well as calculate properties of interest. We identify and repurpose existing molecules, or create new new molecules, for different rare and neglected diseases. Batten Disease, Pitt Hopkins Syndrome, Chordoma and Sialidosis are a few of the rare diseases we have researched extensively.
We are seeking a highly motivated, independent scientist with machine learning and coding expertise as well as chemistry or pharmacological experience to join Collaborations Pharmaceuticals Inc., a locally owned small drug discovery company for a 2-year postdoctoral position. The company is conducting highly interdisciplinary research focused on pre-clinical development of small molecules for rare neurological and infectious diseases.
The purpose of this policy is to document the requirements and responsibilities associated with identifying and managing financial conflicts of interest to safeguard the integrity of Collaborations Pharmaceuticals (Company) research and to comply with federal regulations.
This policy has been developed to address and comply with the specific federal agency requirements as defined in the 2011 Revised Financial Conflict of Interest Regulation, Promoting Objectivity in Research (42 CFR part 50 subpart F).
This policy has been developed to address and comply with the specific federal agency requirements as defined in the 2011 Revised Financial Conflict of Interest Regulation, Promoting Objectivity in Research (42 CFR part 50 subpart F).
Developing new compounds with desirable drug-like properties such as increased target activity while maintaining good ADME is a challenging feat. During the cycle of novel chemical design, chemists are tasked with creation of new analogs from a target molecule. Machine learning (ML) is often integrated into this cycle, providing a way to predict activity and score new compounds according to learned data representation.
One of the best ways to find new drugs is to leverage knowledge of previous attempts, successful and otherwise. Collaborations Pharmaceuticals Inc. have gathered a large collection of openly available data that represents many thousands of different biological targets, and for each of these targets, we have already built and validated the computational models.
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