OUR MISSION
Transforming Chemistry,
Enriching Lives
We use real drug discovery intelligence alongside artificial intelligence to develop clinical candidates for rare, neglected and unmet therapeutic needs.
Our pipeline reflects years of dedicated research, innovative breakthroughs, and a commitment to bringing life-changing treatments to those who need them most.
View Pipeline
OUR TOOLS
Innovative Solutions To Complex Challenges
Whether you’re looking to predict toxicity, visualize molecular data, or build machine learning models, our tools are designed to meet your unique needs.
Not sure which tool is right for you?
Take our short assessment to find the perfect solution for your professional goals.
OUR PARTNERS
Drive Success Through Collaboration
Breakthroughs happen when expertise meets opportunity. At the core of our success lies a commitment to collaboration, partnering with innovators, organizations, and researchers who share our vision for advancing science and transforming lives.
Whether through licensing agreements, strategic partnerships, or tailored contracting solutions, we build relationships that deliver meaningful impact.
Partner With UsWHO WE ARE
Innovators in Machine Learning and ADME/Tox
Collaborations Pharmaceuticals, Inc.® is a privately owned company that performs research and development on innovative therapeutics for multiple rare and neglected infectious diseases. We develop and apply our artificial intelligence (AI) software to aid in drug discovery and toxicology assessment as well as to identify and translate early preclinical to clinical stage assets. Our software can also be used to design new molecules with desired properties. This software can be used by pharmaceutical, consumer product, as well as other companies that require chemistry.
From Automated Reporting To Generative Tools & Beyond
SEE ALL TOOLS
Intelligent report generation, customized to your workflow.
SEE DETAILS
Enables GC/MS spectra predictions for a molecule from structure alone
SEE DETAILS
A platform for the generative design of molecules to create new intellectual property
SEE DETAILS
A suite of tools to curate structure activity data, then build and validate machine learning models
SEE DETAILS
Thousands of machine learning models to help prioritize new uses and off target effects of molecules
SEE DETAILS
A suite of ADME and toxicology machine learning models with optional read across module
SEE DETAILS
Human uptake and efflux transporter machine learning models
SEE DETAILS
Molecule preparation, autocuration and data visualization tools
SEE DETAILS
Acetylcholinesterase machine learning models for multiple species
SEE DETAILS
A comprehensive database of macrolactones and associated biological data
SEE DETAILS
Prediction of the UV-VIS spectra for a molecule from structure alone
SEE DETAILS
A robust chemical inventory tracking software with fine-grained location tracking, advanced querying options, and cross-platform support.
SEE DETAILSPFAS Detection Tool
Enter SMILES strings to check for PFAS classification across multiple regulatory rule sets. Provide your email to receive results.
Results
FEATURED PUBLICATION
MegaTrans—machine learning models for drug transporters corresponding to the FDA guidance
Patricia A. Vignaux, Melanie Tojong, Alexander Kyu, Lucy J. Martinez-Guerrero, Joshua S. Harris, Thomas R. Lane, Stephen H. Wright, Nathan J. Cherrington, Sean Ekins. 2026 Apr; DOI: 10.1016/j.dmd.2026.100293
Regulatory guidances (eg, FDA and European Medicines Agency) require an understanding of the interactions of novel drugs, natural products, and environmental toxicants with key transporters to avoid compounds with undesirable side effects. Computational approaches to predict such interactions using machine learning models trained on in vitro data could prevent compounds that are transporter inhibitors with potential for drug-drug interactions from reaching the more costly development stages. We now describe the curation and machine learning model building for the transporters covered in the FDA guidance (organic anion transporter 1, organic anion transporter 3, organic cation transporter 2, organic anion transporting polypeptide 1B1, organic anion transporting polypeptide 1B3, P-glycoprotein, breast cancer resistance protein, multidrug and toxin extruder 1, and multidrug and toxin extruder protein 2K) that enabled the creation of MegaTrans, a web-based software product that enables users to input molecules and predict the inhibition of transporters of interest.[...]
READ MORE>190
9
3
2
>12
7
WHAT WE DO
No Disease Is Too Small
We apply our machine learning and drug discovery expertise to a wide array of projects both internally and with collaborators. Our toxicology models and read across software could assist your companies sustainable chemistry goals and help you in regulatory filings. We have developed new approaches which can be used to rapidly predict molecular properties for massive DNA-encoded libraries or predict properties for PROTACS. In the process of our work we have raised awareness of the potential for dual use of these AI technologies and therefore have expertise for such evaluations which has caught the attention of government agencies.