Using End-to-End Wet Lab Automation Combined with Machine Learning to Industrialize Drug Discovery


Arctoris is driven by a relentless pursuit to make drug discovery better. An industry where bringing a single drug to the market costs >$2 billion with success rates <5% must be reimagined. The key is to leverage technology — the same tools that have revolutionized many other industries that can propel the drug discovery process into the 21st century. To this end, Arctoris has developed a unified drug discovery platform, Ulysses®, that synergistically combines closed-loop robotics and machine learning to accelerate and improve the drug discovery and development process. Ulysses® conducts drug discovery programs using end-to-end wet lab automation combined with advanced machine learning approaches and deep human expertise. Wet lab experiments are conducted across biochemistry, biophysics, molecular and cellular biology, ranging from fully-automated organoid cell culture and imaging to detailed mechanistic enzymology and custom assay cascades. Arctoris leverages robotics to generate ultra-rapid, large, precise, and fully-annotated datasets — with 100x times more data points per assay compared to industry standard. This means all decisions at each stage of the discovery process, from target to a candidate are powered by robust, reliable, and meaningful data generated at scale and speed.

For the company’s internal R&D, the resulting drug discovery datasets are passed through automated analytical pipelines to train small molecule-focused machine learning models that assimilate the structure-activity relationship to create powerful predictive models for multiparametric optimization from hit to the candidate. Arctoris’ machine learning approach allows more effective leaps in molecular design, resulting in better molecules progressing faster. This is reflected in significantly accelerated Design-Make-Test-Analyze cycles (from weeks down to days), powered by 24-hour data turnaround enabled by the robotic experiment execution.

Leveraging its platform, Arctoris pursues an internal pipeline of assets in oncology and neurodegeneration, while also collaborating with biotech, pharma, and AI drug discovery partners in the US, Europe and Asia-Pacific. Bringing together seasoned biotech and pharma veterans with its proprietary technologies, Arctoris’ approach leads to higher success rates and an accelerated progression of programs toward the clinic. Partners leverage Arctoris’ Ulysses® platform to accelerate their drug discovery programs from target to hit, lead, and candidate. Many partnerships revolve around Arctoris’ sector-defining capabilities to rapidly and reliably generate target and disease-specific wet lab datasets across a range of modalities such as small molecules, RNAi, peptides, antibodies, and PROTACs. Typical use cases include target validation, hit ID and validation, hit-to-lead progression, and lead optimization.

Arctoris and Evariste Technologies announced a joint venture to discover novel small molecule kinase inhibitors for non-small cell lung cancer

Figure 1: Arctoris and Evariste Technologies announced a joint venture to discover novel small molecule kinase inhibitors for non-small cell lung cancer. The partnership brings together two highly synergistic approaches for AI-guided and robotics-powered molecule design to significantly accelerate the DMTA (Design, Make, Test, Analyze) cycle and find superior therapeutic agents faster.

One such partnership is with Evariste Technologies, a London-based AI drug discovery company. The partnership sees the two companies bring together their synergistic strengths in wet lab data generation, data science, and machine learning-enabled molecular design, respectively. During the partnership, Arctoris and Evariste have developed novel inhibitors against cMET, a proto-oncogene and drug target in non-small cell lung cancer. Within only six months, the partnership has progressed from target selection to several qualified lead series with attractive molecular profiles — showcasing how the combination of wet lab automation and machine learning-driven and computational approaches truly accelerate drug discovery and development.

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