AI tools used by life sciences companies

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AI is now firmly integrated throughout the pharmaceutical value chain, from drug discovery to clinical research and manufacturing. According to PwC, approximately 80% of pharmaceutical and life sciences professionals are using AI for drug discovery, and 79% of pharmaceutical executives expect intelligent automation to have a significant impact on their industry over the next five years. Beyond core functions, AI is being used throughout organizations to improve productivity and reduce manual tasks.

“We use Microsoft Copilot across our office productivity tools, and we’ve also built an internal AI agent on the Enterprise Agent Workflow Platform (Dify.ai) across our commercial and corporate functions, including finance, legal, human resources, and IT. AI has streamlined day-to-day operations and freed up our teams to focus on higher-value activities. From contract reviews and financial reporting to recruiting and employee support, AI improves the quality and speed of work while reducing manual labor,” said a Zai Lab spokesperson.

AI for drug discovery

AI is widely used throughout the drug discovery process, from literature searches and target identification to molecular design and validation. It is already used in everyday research and development to improve speed and efficiency. “In our day-to-day research and development operations, we leverage AI to make processes more efficient. This includes using intelligent search to quickly find information across scientific and regulatory materials, supporting our medical and scientific writing teams, and streamlining document reviews for quality and compliance workflows,” said a Zai Lab spokesperson.

One of the key tools used is AlphaFold 3, developed by Isomorphic Labs in partnership with Google DeepMind. This model can predict the structure and interactions of molecules and is used by major pharmaceutical companies to discover small molecules for undisclosed targets. The broader AlphaFold ecosystem has also expanded rapidly. The freely available AlphaFold protein database is used by more than 3 million researchers in more than 190 countries, including more than 1 million users in low- and middle-income countries. In recognition of the impact of this research, he was awarded the Nobel Prize in Chemistry in 2024.

Another major player in this space is XtalPi, which builds an AI-driven automated platform for drug discovery. Its systems are used by large pharmaceutical companies and small biotech companies. The company works with more than 80 partners across academia, pharmaceuticals, and biotechnology. The company’s CSP platform demonstrated industry-leading accuracy in blind testing with Pfizer, contributing to growth and supporting collaborations such as work related to the development of paxlobid during COVID-19.

Insilico Medicine’s AI platform is also widely used by pharmaceutical companies such as Eli Lilly, Menarini Group, Qilu Pharmaceutical, and Servier. The company’s Pharma.AI platform, which includes PandaOmics and Chemistry42, is used to accelerate the identification of preclinical candidates for diseases such as IPF and cancer, often reducing timelines to less than 18 months.

Some companies, such as Zai Lab, are developing unique tools for research and development using custom-designed AI built for specific purposes.

Nanyang Biologics is taking a similar approach. Founded as an AI-first drug discovery company, AI is at the core of our daily compound discovery process.

“We first built the Vecura compound discovery platform to support our internal discovery operations. As our work expanded to include screening, docking, protein structure prediction, bioactivity scoring, ADMET profiling, and molecular design, we needed a practical system that could integrate multiple AI tools into a single workflow, reduce manual handoffs, and allow teams to quickly move from hypothesis to candidate prioritization,” he said. Mr. Duy Trieu, Director of Engineering, Nanyang Biologics.

As the platform has expanded internally, it has also expanded to external users. The company is currently working with 10 to 20 pilot partners.

clinical research

AI is also being applied to clinical research, where timelines, documentation, and compliance processes create delays and operational complexity. Companies are building AI-driven platforms to reduce manual labor and improve clinical trial execution.

Medable develops agent AI systems designed for clinical trials. Agent Studio automates repetitive, compliance-focused tasks such as document validation and trial file management, reducing manual workload. These systems integrate with existing clinical tools and maintain audit trails and human oversight. Its clinical monitoring and TMF agents increase operational efficiency by 70-80%. Medable’s eCOA platform collects patient data remotely and onsite and uses AI to accelerate research construction and translation. AI PI Summary Agent monitors participant data and provides insights to researchers. The platform has been reported to increase participant compliance by over 90%, reduce translation timelines by 43%, and speed trial setup. To date, it has been implemented in nearly 400 trials across 70 countries and 120 languages, covering more than 1 million patients.

Orkin French-American artificial intelligence and biotechnology company aimed at identifying new treatments, optimizing clinical trials, and developing AI diagnostics; is applying AI to clinical trials and biomarker discovery. Its tools analyze pathology data, trained on datasets from more than 800 hospitals, to identify cell types, gene expression, and biomarkers. Eight of the world’s top 10 pharmaceutical companies use the company’s systems.

ICON plc, a multinational medical intelligence and clinical research organization headquartered in Ireland, is also integrating AI into its trials. The Meridian platform brings together data from multiple systems into a single interface to highlight site-level risks and reduce administrative burden.

“Meridian is a next-generation monitoring environment designed specifically for CRAs and operations teams. Meridian brings together information from multiple systems into a single workspace, presents protocol guidance in context, and highlights emerging site-level risk signals. This reduces administrative burden and allows experienced monitors to focus on monitoring, quality, and proactive intervention rather than system navigation.” Tony Clarke, Senior Vice President of Enterprise AI at ICON, said:

These capabilities are supported by ICON’s agent AI platform, Orbis, which connects workflows across trial initiation, execution, and closure, enabling system-wide coordination under human oversight.

Medidata, part of Dassault Systèmes, provides a cloud platform to more than 2,200 life sciences organizations, including pharmaceutical, biotech, medical technology, and CRO. Its products include Medidata Rave (electronic data capture), Clinical Data Studio (AI-driven data management), patient tools such as myMedidata and eCOA (electronic clinical outcome assessment), and analyzes such as synthetic control groups and trial feasibility. Currently, 18 of the world’s top 25 pharmaceutical companies use Medidata.

manufacturing industry

AI is being used in pharmaceutical manufacturing to improve efficiency, quality, and compliance across production and supply chains.

Aizon provides tools for anomaly detection, batch optimization, and performance management. Its platform supports batch execution, real-time monitoring, and yield optimization and is used by CDMOs, large biopharmaceutical companies, and small biotech companies.

Veeva Systems supports quality and regulatory workflows through products such as Vault QMS, Vault LIMS, Batch Release, and Vault RIM to help streamline processes from development to distribution. The company serves more than 1,500 customers across pharmaceutical and biotechnology.

Aragen Life Sciences, a leading partner providing R&D and manufacturing solutions from India to the global life sciences industry, is also embedding AI across the CRDMO value chain through a structured framework aligned to business outcomes. Its applications include an AI-powered lead optimization platform that predicts pharmacokinetic properties and accelerates compound development, and golden batch analysis that uses machine learning to improve yield, quality, and production efficiency.

“The company also uses an AI-enabled sourcing platform for supplier discovery and pricing, a GenAI-powered electronic lab notebook to structure and analyze lab data, a literature discovery engine to extract insights from global research, and an AI-driven proposal management system to expedite RFI and RFP responses.” Swapnil Wadhwa, Chief Digital Officer, Aragen Life Sciences.

These are just some of the tools life sciences companies are using today. A growing number of startups are addressing bottlenecks across the pharmaceutical value chain, and some estimates suggest that AI could save the industry more than $50 billion annually in research and development costs. It remains to be seen how far and how quickly this will expand.

Ayesha Siddiqui