Artificial Intelligence (AI) has been transforming various industries, and biotechnology is no exception. The integration of AI in biotech has opened new avenues for personalized medicine, predictive diagnostics, and cutting-edge research. This blog explores how AI revolutionizes the biotech industry, highlighting innovative companies leveraging AI for significant advancements in healthcare and beyond.

ADCs: AI-Driven Target Identification and Optimization

One of AI’s most promising applications in biotechnology is developing antibody-drug conjugates (ADCs). ADCs combine antibodies’ specificity with cytotoxins’ potency. AI optimizes ADC design by predicting the most effective antibody targets and minimizing off-target effects through epitope prediction and large-scale in silico screening of predicted candidates.

  • Lantern Pharma: Their RADR® platform integrates pre-clinical and clinical data to identify targets and predict drug responses, reducing development risks and enhancing ADC effectiveness. They partnered with Bielefeld University to create high-potential ADCs.
  • Absci: Absci’s generative AI platform combines data, AI, and wet lab validation to create and validate millions of de novo antibody designs weekly, accelerating novel molecule discovery and disease targets, as well as clinical trial outcome predictions.
  • Merck: Collaborating with Biologic Design, Merck uses their AI to develop multispecific antibodies for oncology and immunology. Biolojic’s platform employs AI and computational techniques to engineer human antibodies into programmable switches that perform specific functions such as agonism, antagonism, and conditional binding. Merck’s partnership with Caris Discovery involves identifying and developing new ADC targets using their ADAPT Biotargeting platform, which combines clinical and genomic data with AI algorithms to pinpoint early signs of disease and identify new therapeutic targets.

Gene Editing & Gene Therapy: AI-Guided Precision & Efficiency

Transitioning from ADCs, AI in biotechnology is also making significant strides in gene editing and therapy. AI advances gene editing and therapy, improving techniques like CRISPR and AlphaFold for protein structure prediction and gene pattern editing. 

  • Profulent: Leveraging the power of AI, they have developed the OpenCRISPR-1, an open-source gene editor using large language models (LLMs) trained on extensive biological sequences and contexts. This innovation has the potential to revolutionize the field of gene editing. 
  • Dyno Therapeutics: Their CapsidMap technology, powered by AI, optimizes adeno-associated virus (AAV) capsid designs. This creates safer, more effective gene therapy vectors applicable to various diseases. They partner with major developers, including Astellas, Novartis, Roche, and Sarepta, to tackle muscle, eye, and central nervous system disorders. 
  • Form Bio: Developing the FORMsight AI platform that uses advanced, customizable AI models trained on AAV datasets, they optimize AAV construct designs, simulate production, enhance AAV capsids for improved transfection efficiency and tissue targeting, and predict the efficiency of encapsulating complete constructs within the capsids. They partner with Ginkgo Bioworks to integrate seamless AI into manufacturing

Cell Therapy: AI-Assisted Manufacturing and Delivery

From gene editing to cell therapy, AI in biotechnology optimizes cell therapy manufacturing and delivery. AI aids in genetic construct design, process optimization, and real-time therapeutic efficacy monitoring. 

  • Achilles Therapeutics: Their PELEUS platform uses proprietary real-world data and identifies the most potent clonal neoantigens for personalized cancer therapies, integrating AI with their manufacturing process, VELOS, to produce clonal neoantigen-reactive T cells or cNeT.
  • CellVoyant: Using AI imaging platforms, they optimize stem cell differentiation and monitor cell development and behavior, reducing costs and accelerating therapy development. This innovative approach enables the company to predict and control the fate of stem cells in real time, allowing for the efficient and scalable manufacturing of various cell types.
  • AI: Their AlphaStem platform leverages AI to produce human tissue for cell therapies. The platform creates a virtuous cycle by generating therapies, producing massive data that further improve cell replacement therapies, targeting diseases like diabetes, obesity, and muscular dystrophy. Their proprietary digital twin of the embryo allows them to surface actionable insights and quickly iterate on protocols, leading to significant improvements in cell differentiation and purity.

Different AI-Designed Drugs in Clinical Trials

The impact of AI in biotechnology is already being felt, with several AI-designed drugs entering clinical trials. Here are a few examples:

  • Insilico Medicine: Their AI-designed pan-fibrotic inhibitor is in Phase II trials.
  • Atomwise: Their AI-designed small molecule drug for cancer treatment is in Phase I trials.
  • Exscientia: Their AI-designed Parkinson’s drug is in Phase I trials.
  • Iktos: Their AI-designed autoimmune disease drug is in Phase II trials.


These developments demonstrate AI’s potential to accelerate the discovery and development of new treatments, and we can expect to see more AI-designed drugs enter clinical trials soon.


AI is revolutionizing biotechnology landscape by enhancing the precision, efficiency, and personalization of ADCs, gene editing, and cell therapies.

The time has come for AI applications to flourish, with growing instances of impact, as we reach a tipping point fueled by an explosion of biological data, increasing computational power, advanced in vitro models, automated wet-lab processes, and promising initial clinical results. Embedding digital and analytics in R&D is crucial for success and patient value. AI and advanced analytics can boost R&D returns by increasing speed, reducing failures, cutting costs, and enabling sustainable tech platforms. Furthermore, the next five years will be crucial in demonstrating the long-term viability of ADCs and CGT’s as widely applicable treatment options.

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Written by

Akanksha Agarwal
Senior Consultant

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