AI becomes central to biotech discovery and drug development
Biotech is embedding AI into R&D to speed drug discovery, improve accuracy, and streamline research with integrated systems and skilled teams.
The biotechnology industry is moving from early AI experimentation to fully integrated discovery systems that embed AI into everyday research operations.
According to the 2026 Biotech AI Report from Benchling, leading organisations are reshaping data environments and R&D structures, making AI a core part of the drug development process.
Predictive models, such as protein structure prediction and docking simulations, are accelerating early-stage discovery, helping scientists identify targets faster and improve accuracy.
Challenges persist in generative design, biomarker analysis, and ADME prediction, where adoption lags due to fragmented or poor-quality data.
Organisations overcoming these hurdles invest in high-quality, well-annotated measurements and strong integration between wet and dry lab work. It creates a continuous learning cycle that drives faster insights and reduces experimental dead ends.
Talent strategies are evolving to place AI expertise directly in R&D teams. Many firms upskill existing scientific staff to act as ‘scientific translators,’ bridging biology, regulatory needs, and machine learning.
Embedding AI leadership within research teams or using hybrid models reduces handoffs and ensures AI tools remain practical in real-world experiments.
Biotech firms combine in-house development with commercial components, following a ‘build what differentiates, buy what scales’ strategy. Confidence in AI is rising, driving investment in infrastructure, modelling, and integrated AI workflows for research.
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