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Absci Develops Groundbreaking Machine Learning Models for In-Silico Antibody Design, Powered by NVIDIA

Absci to present in-silico drug discovery technology at NVIDIA GTC
Breakthrough ML achievements highlight the viability of the computational lead optimization pipeline
Absci collaborates with NVIDIA on research to accelerate and scale in-silico drug discovery platform

Absci Corporation, a drug and target discovery company harnessing deep learning, AI and synthetic biology to expand the therapeutic potential of proteins, announced the development of machine learning models for designing and refining novel therapeutic antibodies. The new research demonstrates progress towards a fully in-silico machine learning pipeline for drug discovery.

Absci is pursuing a vision for fully in-silico biologic drug design, going from target to drug candidate sequence ‘with the click of a button.’ The ability to realize this vision is built on Absci’s proprietary data generation technologies that can evaluate up to ten billion individual cells per week – each expressing a drug sequence variant – which provide relevant, high-quality substrate data for AI applications.

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In today’s session at NVIDIA GTC, a global AI conference, Absci Lead AI Scientist Joshua Meier will describe two of the company’s drug discovery ML breakthroughs and present validation of Absci’s in-silico lead optimization models. The validation, requiring wet-lab testing of model predictions, was enabled by Absci’s proprietary and highly engineered synthetic biology platform and assays.

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  • The first breakthrough is a machine learning model for quantitative prediction of antibody target affinity, allowing computational predictions of binding strength. In one example, Absci demonstrated that this model could accurately predict affinity across four orders of magnitude for previously unseen trastuzumab variants, including accurately predicting variants that had better target affinities than wild-type trastuzumab.
  • The second breakthrough is a machine learning model to score ‘naturalness’ of antibody variants; naturalness is a parameter that Absci shows is associated with multiple developability characteristics, and antibodies with better developability have a better likelihood of success as drug candidates through preclinical testing and clinical development.
  • Further, Absci showed that generative ML techniques enabled simultaneous in-silico optimization for both affinity and naturalness, underscoring the viability of the computational lead optimization pipeline.

Absci is collaborating with NVIDIA to accelerate and scale Absci’s in-silico ML pipeline, using NVIDIA expertise and optimized graph- and transformer kernels on NVIDIA A100 Tensor Core GPUs. In addition, Absci will continue to synthesize and test the predictions from the models in its proprietary ultra-throughput assays to further validate, iteratively train, and refine the AI performance.

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Sean McClain, founder and CEO of Absci, said, “We are thrilled to be collaborating with NVIDIA to advance the field and enable the development of better medicines. Our AI breakthroughs stand on the shoulders of our tremendously differentiated synthetic biology platform and breakthrough assays – the data generation engine that fuels our ML training and validation. Based on this powerful Absci technology, along with NVIDIA’s scientific and technical expertise and compute resources, we are looking forward to scaling the advancements of our in-silico lead optimization models to generalize across the wide breadth of target classes, therapeutic modalities, and developability parameters relevant for biopharmaceutical discovery.”

“Our collaboration with Absci is aimed at enabling the most advanced AI paradigms and accelerating protein design,” said Kimberly Powell, vice president of healthcare at NVIDIA. “Absci’s powerful data generation and AI protein engineering platform is already helping the drug discovery industry, and NVIDIA technologies will help power and scale Absci’s fully in-silico platform.”

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[To share your insights with us, please write to sghosh@martechseries.com]

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