Capabilities

Nucleic Acid Medicines & RNA Biology

The team has significant experience in Nucleic-Acid-Medicine modality (ASOs, SSOs, siRNAs & mRNA gene therapies) and RNA Biology. We are leading industry experts in related NGS assays, Foundation Models in Proteins & RNA & relevant Functional Genomics.

We consult in ASO, SSO & siRNA design; from assays to create the right datasets (in vitro, in vivo, cell-free) to be able to nominate drug candidates robustly employing unambiguous readouts, to the right chemical modification patterns to adopt state-of-the-art best practices in PK/PD and pharmacology profile. For example, we have created and published on rigorous methods for off-target and IC50 determination for oligonucleotides using NGS assays, see here. Also, see past publications here and here on sequence-chemistry collusion in oligonucleotide pharmacology. The team also have significant experience in aptamers and peptides, especially, in computationally enhanced screening paradigms using modern ML/AI tools (Diffusion Models, Latent Space Methods, Active Learning). Please reach out to tap into our extensive CRO network for oligonucleotide R&D.

AI, Machine Learning & Signal Processing

AI and Machine Learning applied to real-world data — for example, lab experiments, financial markets, biology, material science and engineering — is dictated by the nature of information flow and demands systems-level thinking within the respective domain.

We are tool-builders, not simply tool-users.

As a result, our customers are not abandoned with technical debt and unnecessary complexity.

We simplify to a fault. Occam’s Razor is our butter knife!

Scientific Computing, HPC & Modern Infrastructure

We bring collective industry expertise in delivering coherent, reproducible scientific workflows using GPU acceleration in HPC and cloud infrastructure with modern DevOps practices, especially in computational chemistry, molecular modeling, Bio- and Chem- informatics.

Detective work on the Data Generation Process

We are Sherlocks who delve into the Data Generation Process and not just the output data. We care as much about the physicality and provenance of how the data was gathered, the stochastic processes governing the signal to noise characteristics, as we care about the statistics of measurements and the insights distilled from it.

This means, for example, that in a lab-in-a-loop setting we come up with better Design of Experiments, Active Learning strategies and Bayesian Optimization policies— ultimately decreasing the experimental budget and increasing the information content in every experiment.

We supercharge your internal teams.