Computer-Aided Drug Design (CADD): Smarter Drug Discovery Starts Here
In early-stage drug discovery, every decision matters — and every failed synthesis costs time, resources, and momentum. At Oxeltis, we believe that designing better molecules begins long before a chemist sets foot in the lab. That is why, through our strategic consortium with Molecular Forecaster, a world-class CADD specialist, we integrate computational drug design at the very heart of our medicinal chemistry programs.
01 — The first step
What Is CADD and Why Does It Matter?
Computer-Aided Drug Design encompasses a suite of powerful computational techniques — structure-based drug design (SBDD), ligand-based drug design (LBDD), molecular docking, free energy perturbation, pharmacophore modeling, and ADMET prediction — that allow scientists to model, predict, and optimize molecular interactions in silico before committing to chemical synthesis.
In the context of early drug discovery, CADD addresses one of the field's most persistent challenges: the vast, largely unexplored chemical space. With hundreds of thousands of potential scaffolds and millions of possible analogs, rational prioritization is not just an advantage — it is a necessity. By simulating how candidate molecules interact with their biological targets at the atomic level, CADD narrows the field to the most promising candidates, dramatically improving the signal-to-noise ratio of any hit identification or lead optimization campaign.
02 — The partnership
A Seamlessly Integrated Approach
Through our partnership with Molecular Forecaster, Oxeltis clients gain access to proprietary CADD software and deep computational expertise that has been purpose-built for small molecule drug discovery. Molecular Forecaster's tools are designed to go beyond standard docking scores, delivering physics-based predictions of binding affinity, selectivity, and molecular stability that translate directly into actionable medicinal chemistry insights.
This integration is not merely additive — it is transformative. Our medicinal chemists work in close, iterative dialogue with Molecular Forecaster's computational scientists to create a seamless design-make-test cycle. A virtual screening run informs a focused synthesis batch; docking poses guide regioselectivity decisions; predicted ADMET profiles help eliminate liabilities early. The result is a leaner, faster, and far more cost-effective discovery program.
The Business Case for CADD in Early-Stage Projects
Unlocking chemical diversity
The numbers speak for themselves. Industry data consistently shows that incorporating CADD at the hit-to-lead and lead optimization stages can reduce the number of compounds synthesized by 30 to 50% while simultaneously increasing the hit rate of bioactive molecules. For any organization working under time pressure and budget constraints — whether a biotech startup or a large pharmaceutical company — this translates directly into accelerated timelines, lower attrition, and a stronger compound portfolio entering preclinical development.
Furthermore, CADD enables the exploration of chemical matter that would be synthetically challenging or prohibitively expensive to prepare without prior computational validation. Virtual screening across large chemical libraries, de novo molecular generation, and scaffold hopping are capabilities that fundamentally expand the scope of what is achievable in any discovery program.
A Full-Spectrum Consortium at Your Service
Bridging Theory and Practice
Oxeltis, Molecular Forecaster, and Partex.ai — our partner for biological assay services — form a fully integrated CRO consortium covering the three essential pillars of early drug discovery: in silico design, organic synthesis, and biological validation. This end-to-end capability means your project moves forward with continuity, scientific coherence, and minimal friction between disciplines.
Ready to accelerate your drug discovery program?
Contact the Oxeltis team today to discuss how our CADD-integrated approach can help you identify and optimize your next candidate — faster, smarter, and with greater confidence.