Emerging AI solutions shaping Life Sciences in 2026

AI in Life Sciences has moved far beyond experimentation. Today, leading pharma and biotech teams are using AI to accelerate drug discovery, streamline clinical development, and unlock new insights from multi-modal scientific data. But choosing the right tools remains difficult, with dozens of platforms promising automation, integration, and faster decision-making processes. 

Across the pharma value chain, a handful of AI solutions are setting new standards for speed, reproducibility, and scientific rigour. To save you some time and help you select the right tool, we have created a list of five emerging AI solutions that are already delivering value at scale across the entire value chain.

Visium: Enterprise agentic AI for Life Sciences

Visium is a Swiss AI company helping global enterprises deploy production-grade, compliant, and scalable AI solutions. Since 2018, they have delivered over 250 solutions for global leaders like Roche, Novartis, Nestlé, and dsm-firmenich; helping them drive efficiency, unlock new revenue streams, and scale solutions that deliver long-term value.

Some of their most recent work includes applying AI to improve predictability in protein design, enhancing digital pathology workflows with Roche, and developing new approaches to defining patient-reported outcome measures with Idorsia.

Visium’s AI Platform for Life Sciences is an enterprise-grade agentic AI platform designed to operationalise AI across regulated Life Sciences workflows. Rather than focusing on a single use case, the platform enables organisations to design and run intelligent workflows that replace legacy processes with AI-supported execution across regulatory, quality, scientific, and commercial functions. Through conversational AI agents, teams can access and work with enterprise data using natural language, reducing manual effort while maintaining accuracy and traceability.

The platform also provides the infrastructure required to operate AI reliably at scale. This includes workflow orchestration, access controls, monitoring, and auditability, all within the organisation’s existing data environment. 

Key features:

• Agentic AI architecture designed for regulated Life Sciences workflows, with built-in guardrails and access controls
• Configurable AI agents and pre-built agent library, supporting clinical, regulatory, quality, scientific, and commercial use cases
• Workflow orchestration in a secure no-code environment, enabling structured collaboration between humans and AI agents
• Centralised governance and auditability, including permissions management, usage tracking, and full activity logging
• Multi-Modal Data Capture at the Source (text, voice, photo) independent from QMS system
• Automated Root Cause Analysis & CAPA Design leveraging your institutional knowledge

Use cases:

• Regulatory document drafting, generating, reviewing and quality assuring first drafts of regulatory documents such as CTD modules, clinical study reports, and periodic quality reviews based on internal data, approved templates, and current regulatory guidance
• Deviation management and investigation, classifying and prioritising deviations, identifying likely root causes through pattern analysis, and documenting remediation actions with full traceability
• Scientific and health literature research, querying internal knowledge bases and external scientific sources to support evidence gathering across R&D and medical functions
• Lab Notebook Assistant, querying experiment records from electronic or scanned paper notebooks (e.g., on Benchlining) to retrieve past details, protocols, and outcomes.

NumerionLabs: AI for drug discovery

Founded in 2012, NumerionLabs is one of the early pioneers of applying deep learning to small-molecule drug discovery. The company is widely recognised for its structure-based screening approach and long-standing collaborations across pharma, biotech, and academic research.

NumerionLabs focuses on early-stage drug discovery, using machine learning to support structure-based small-molecule design. The platform helps research teams prioritise promising compounds by predicting how molecules are likely to interact with biological targets, reducing the reliance on exhaustive laboratory screening.

By applying computational screening at scale, NumerionLabs enables organisations to narrow large chemical spaces before experimental validation. This allows teams to focus resources on higher-confidence candidates and accelerate the initial phases of drug discovery.

Key features:

• AtomNet deep-learning core for modelling 3D molecular structures and predicting protein–ligand interactions
• High-throughput virtual screening to evaluate large chemical spaces and prioritise promising compounds early
• Support for multiple targets and disease areas, including difficult-to-drug targets
• Computational lead optimisation through iterative design and evaluation cycles
• Scalable data and compute infrastructure for handling large molecular datasets and model inference‍

Use cases:

• High-throughput virtual screening
• Undruggable targets
• Early-stage small-molecule drug discovery
• Target-based screening in preclinical research

Owkin: AI for clinical trials & biomarker discovery

Owkin focuses on applying AI to clinical and translational research, with a strong emphasis on privacy-preserving data collaboration. The company has built a strong presence in Europe by working closely with hospitals, research institutions, and pharma partners in highly regulated environments.

They are pioneering advanced agentic AI that can process vast biological data, uncover hidden causal relationships, and generate new actionable insights. 

The platform, including Owkin K Pro, integrates AI agents with rich multimodal data from academic networks to generate insights, predict outcomes, and support decisions in drug development. It employs federated learning to analyze siloed datasets like biomedical images, genomics, and clinical records without compromising privacy.

Key features:

• Federated learning framework enabling model training across distributed clinical datasets
• Multi-modal data analysis, combining clinical, imaging, and omics data
• Privacy-preserving architecture designed for regulated healthcare environments
• Multiomics Navigator that filters, groups, and visualizes patient data with clinical/molecular features for survival analysis and trends.

Use cases:

• Target identification and validation, using multi-modal data to support early discovery decisions and assess potential toxicity risks
• Hypothesis generation and optimisation, enabling teams to test biological hypotheses and refine drug positioning using multi-omics data
• Biomarker and endotype discovery, combining imaging, genomics, and clinical data to support precision medicine
• Spatial omics analysis, enabling oncology insights and publication-ready research outputs

Insilico Medicine (PharmaAI): AI‑powered target identification & pharmaceutical research

Insilico Medicine develops AI-driven platforms that span multiple stages of drug discovery and early development. The company is well known for combining generative modelling with biological data analysis and for advancing several AI-designed compounds into preclinical and clinical pipelines.

Pharma.AI is Insilico Medicine's end-to-end generative AI software and automation platform designed to improve the quality and productivity of pharmaceutical research. It brings together tools for analysing biological and multi-omics data, identifying therapeutic targets, designing new molecular structures, and assessing their potential before experimental testing.

The platform includes components such as PandaOmics for target and biomarker discovery, Chemistry42 for generative molecule design, and additional analytical modules that help prioritise targets with evidence-based scoring.

Key features:

• Target discovery and multi-omics analysis via PandaOmics to prioritise disease targets and biomarkers using diverse biological data sources. 
• Generative molecule design (Chemistry42) for de novo generation, optimisation, and evaluation of small-molecule candidates.
• Clinical trial outcome forecasting (inClinico) to assess the probability of success and identify potential risks in trial design.
• Nach01 multimodal model supporting text, molecular (2D SMILES), and 3D structural data for property prediction, hit discovery, and lead optimisation
• Supporting tools for research workflows, such as DORA for scientific drafting, ChatPandaGPT for knowledge queries, and MMAI Gym for improving large language model performance

Use cases:

• Discovery of novel therapeutic targets with validated biological and safety profiles
• Generating candidate drug molecules for fibrosis or oncology pipelines
• Scientific writing for internal submissions or external publications
• Trial success rates forecasting and portfolio risks identification

PathAI (AISight): AI for diagnostics & pathology

Founded in 2016, PathAI applies machine learning to digital pathology with the goal of improving the consistency and quality of tissue-based analysis. They work closely with clinical laboratories and biopharma partners, positioning pathology data as a reliable input for diagnostics, research, and drug development.

PathAI's AISight is a cloud-based digital pathology platform designed to streamline end-to-end workflows in anatomic pathology labs. It serves as a central hub for case management, image management, and AI integration, enabling pathologists to handle whole-slide images (WSIs), prioritize cases via Intelligent Caselist, and perform real-time collaboration through AISight Live for tumor boards or remote reviews.

By standardising image interpretation and quantifying morphological features that can be difficult to assess manually, PathAI’s technology is used in both clinical research settings and collaborations with biopharma companies. 

Key features:

• End-to-end automation that handles case assignment, slide viewing, and routing across labs or networks without on-premises hardware.
• Quantitative biomarker scoring for research and trial applications
• AI-assisted analysis of digital pathology slides, enabling consistent and reproducible tissue assessment
• Automated tissue and cell feature extraction, supporting detailed morphological analysis
• Integration with digital pathology workflows, including slide management and remote review

Use cases:

• Primary diagnostic workflows based on whole-slide image review for cancer and disease assessment
• Case prioritisation and workload management in high-volume pathology laboratories
• Translational research and biomarker studies using quantitative tissue and tumour microenvironment analysis
• Cohort analysis for biopharma research, supporting pathology-driven discovery and clinical development

Driving innovation across Life Sciences with AI

AI has already become an integral part of how Life Sciences organisations operate, but its impact varies widely depending on where and how it is applied. 

The solutions highlighted in this overview reflect the different ways AI is being used across the Life Sciences value chain. From specialised platforms advancing drug discovery and pathology, to enterprise systems designed to integrate AI into regulated, real-world workflows, each addresses a distinct set of operational and scientific challenges.

As AI continues to evolve, the focus will shift from experimenting with possibilities to building solutions that teams rely on every day. Those that combine depth, reliability, and flexibility will play a central role in shaping the future of life sciences innovation.

If you are exploring how to apply AI across your own Life Sciences workflows, our team is always happy to exchange ideas and share practical insights from regulated, real-world implementations.

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