Drug development in biotech carries inherent risks, with roughly 90% of candidates failing to reach the clinic, a reality that hasn't fundamentally changed despite technological advances1. Yet, recently, early clinical trial results from artificial intelligence (AI)-developed drugs are beginning to demonstrate some green shoots of success, gradually shifting how the biotech industry views this emerging capability2,,3.
The core barriers to drug development success in the real world rely on accurately predicting molecular behavior, deciphering complex disease mechanisms and understanding how patients will respond. AI is now addressing these challenges directly. By processing vast biological datasets, from genetic networks to patient outcomes, AI enables teams to identify better disease targets, design more effective compounds and map out predictive patient responses.
Leveraging AI into drug development workflows is not an option, yet the competitive advantage will no longer come from having the largest model or the most published research. Instead, success belongs to those who translate AI capabilities into actual medicines, where scientific rigor, technical expertise, and strong leadership converge. This requires the right talent in key executive positions to bridge the gap between algorithmic potential and clinical reality.
This blog examines where AI is creating tangible R&D impact, investment trends, early field wins, and how Occam Global partners with innovators to build the leadership needed to transform ideas into meaningful patient outcomes.
Creating a Biotech R&D Edge with AI
AI’s most profound impact addresses the core challenge of biotech, notably long, costly, and uncertain R&D success rates. By automating complex analyses and leveraging predictive models, AI improves efficiency and increases the probability of success, tackling the high failure rates and extensive experimental burden that have long defined the industry.
Key R&D Gains Enabled by AI
Accelerating Discovery Through High-Throughput and In Silico Systems
AI is dramatically compressing drug discovery timelines. Insilico Medicine's AI-discovered fibrosis drug entered Phase II trials in just 12 months, 85% faster than traditional methods, setting a new standard for efficiency4. This acceleration stems from AI's ability to process massive datasets spanning genomics, proteomics, and metabolomics to identify potential drug targets that might otherwise elude human researchers.
By automating the most time-consuming analytical work, these systems allow researchers to focus on hypothesis validation rather than data exploration, fundamentally reshaping how discovery teams operate.
Improved Molecular Design and Virtual Screening
Generative models like Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) propose novel molecular structures and predict protein interactions with unprecedented accuracy. Virtual screening rapidly identifies promising candidates, dramatically reducing the need for labor-intensive wet-lab testing. Chemify exemplifies this integration, fusing chemistry, robotics, computation, and machine learning through a proprietary programming language to digitize molecule creation. This approach enables chemists to validate hypotheses systematically rather than exploring blindly through traditional high-throughput screening.
The result is a more efficient pipeline with fewer candidates failing at the bench, fewer make it to expensive preclinical testing without promise, and more resources flow toward compounds with genuine potential.
Informed Clinical Trial Design and Risk Mitigation
AI doesn't stop at discovery, rather it extends to clinical development. By analyzing preclinical and clinical datasets, AI identifies biomarkers and patient subpopulations most likely to respond to treatment, enabling more targeted patient recruitment. Adaptive trial designs powered by real-time monitoring improve both efficiency and outcomes.
This predictive capability is transformative. It means fewer patients exposed to unsafe compounds, faster trial readouts, and fewer late-stage failures that drain resources and morale.
Integration of AI and Robotics for Manufacturing and Quality Control
Beyond discovery and development, AI is optimizing the manufacturing and quality assurance processes that bring medicines to scale. AI-driven systems and companies streamline quality control by detecting anomalies in production data, flagging deviations before they become compliance issues, and predicting where manufacturing bottlenecks will occur.
When combined with robotics, these systems reduce human error, accelerate scale-up timelines, and ensure consistent product quality. Pickle Robot is a standout here, applying Physical AI to automate truck unloading and develop adaptable autonomy that enables seamless robot coordination across logistics operations. This integration is particularly valuable for biotech companies scaling from pilot production to commercial manufacturing, a notoriously complex and costly transition.
AI-Native Valuations and Funding Trends
Venture capital is increasingly recognizing AI’s tangible, measurable impact in accelerating and de-risking drug development. In the latest Pitchbook reporting5, and partnership extensions we see the investment patterns reflect growing confidence in AI’s potential:
- Valuation Premiums: In 2024, median valuations for AI-native firms hit $78M, nearly double the $40M median for traditional biotech.
- Larger Deal Sizes: Median VC deal sizes for AI-native biotech reached $21.8M, versus $13.7M for the broader sector.
- Surge in Investment: VCs deployed $3.2B across 135 AI-driven drug development deals in the past year. Over five years, AI-biotech venture funding jumped from $1.9B to $12.5B.
- Pharma Partnership Growth: Insitro, has extended their research collaboration with Bristol Myers Squibb leveraging their ChemML Discovery Platform6.
- Notable Rounds: $50M Series B to Chemify7, $250M Series A to Lila Science
AI in Action Driving Biotech Advances
The following examples show how new players, integrated platforms, and real-world tools are reshaping the pace and possibility of AI-powered scientific innovation.
New Players and Tools
The ecosystem is expanding rapidly. Tech giants like OpenAI and Anthropic are turning their attention to life sciences, bringing talent, ideas, and novel tools that accelerate innovation.
Integration Unlocks Innovation
AI is moving beyond isolated breakthroughs. Firms like Generate:Biomedicines are integrating biology, chemistry, clinical operations, and computation into a connected system that accelerates discovery. Chemify is fusing chemistry, robotics, computation, machine learning, and Chemify's programming language to digitize molecule creation.
Real-World Adoption & Trust Building
Novel AI-powered tools like Form Bio, ModernVivo, and Potato are helping scientists streamline scientific workflows and improve research outcomes. By embedding AI seamlessly into daily workflows, these tools build familiarity and trust through immediate, visible impact. Scientists encounter AI solutions so naturally embedded in their routines that adoption feels intuitive rather than disruptive, ultimately normalizing AI as an essential part of modern R&D.
Occam Global Placing The Right Leadership for Your AI-Native Biotech
We’re at an inflection point. The next era of innovation won’t be defined by who builds the biggest models, but by how we turn insight into real impact, combining science, technology, and human ingenuity to bring better medicines to patients faster. Real impact requires leadership capable of translating AI potential into meaningful patient outcomes.
Our placements span firms that are now leading the AI-native wave, from early-stage life science disruptors to platform leaders driving multi-million dollar deals.
DISCOVERY FOCUSED COMPANIES
RECURSION (US) Developing drug discovery platforms and pipelines with machine learning.
o Multiple Board Members; COO; CMO, CCO, VP Regulatory
RELATION (UK) Utilizing machine learning and experimental technologies to discover drugs for pressing unmet needs.
o CSO; CTO
NUCLEOME (UK) Combining 3D genome technology and machine learning to link genes to diseases and map pathways precisely for drug discovery.
ATOMIC AI (US) Exploiting the fusion of artificial intelligence and structural biology to unlock RNA drug discovery.
o CSO
INSITRO (US) A data-driven drug discovery and development company that leverages machine learning and high-throughput biology to transform how medicines are created to help patients.
o CSO; CDO; Head of People; Head of Neuroscience Discovery; SVP Drug Discovery; SVP Research Operations; VP Molecular Design
ENVISAGENICS (US) Envisagenics’ AI platform, SpliceCore®, powers the discovery and therapeutic targeting of RNA splicing — unlocking breakthrough treatments for patients.
o CBO
OROGEN (US) Orogen Therapeutics is a drug discovery company that uses proprietary AI-enabled DNA-encoded library (DEL) technology to identify novel chemical matter.
o CEO
ZEBIAI (US) Applying experimental DNA encoded library data sets to power machine learning for drug discovery.
BIOXCEL THERAPEUTICS (US) Utilizing artificial intelligence approaches to develop transformative medicines in neuroscience and immuno-oncology.
o Board Member
DEEP GENOMICS (CANADA) Aims to revolutionize drug development by leveraging expertise in artificial intelligence (AI) to decode RNA biology.
o CTO
NEPTUNE BIO (US) Finding combinatorial cures by pinpointing unique conditions that drive cellular change by combining novel biological data with computational AI to model complex gene synergies to unlock new combinatorial therapies.
o CEO
BASECAMP RESEARCH (UK) Building an AI agent that answers any question related to biology and the biodiversity of the natural world.
o CBO
o VP BD
EVERYONE MEDICINES: AI-driven design and development process allows us to create individualized medicines to correct unique genetic variants that cause devastating diseases.
DESIGN-FOCUSED COMPANIES
LABGENIUS (UK) The first biopharmaceutical company developing next-generation protein therapeutics using a machine learning-driven evolution engine (EVA™).
CHARM THERAPEUTICS (UK) Delivering transformational medicines through 3D deep learning and cutting-edge drug discovery technologies.
ANAGENEX (US) Evolving new small molecule medicines by combining ultra-high throughput biochemistry and machine learning.
o Board Member; CSO; CTO, Head of Computational Chemistry
CHEMIFY (UK) Digitizing chemistry, including using artificial intelligence to explore the trillions of possible combinations of natural elements in chemical space.
o COO, CTO; CTO 2025, SVP Sales & BD;
IKTOS (FRANCE) Specializes in developing AI solutions applied to chemical research, specifically medicinal chemistry and new drug design.
o CBO
PEPTONE (UK & SWITZERLAND) Developing Oppenheimer, an AI protein-modeling platform that can transform un-druggable intrinsically disordered proteins (IDPs) into developable drug candidates
LATENT LABS (UK) A frontier AI lab building generative models that capture the fundamentals of biology.
A-ALPHA BIO (US) Harnesses synthetic biology and machine learning to engineer high-impact medicines precisely.
o CBO
CLINICAL TRIAL INNOVATORS
GSK.AI (US/UK) Using advanced machine learning and AI applications to enhance drug discovery and provide critical insights that increase the probability of success in the clinic.
o VP AI/ML; VP Data Science & Analytics;
UNLEARN AI (US) Using generative AI to enhance clinical studies' efficiency, ethics, and reliability.
o Board Member; Head of People
Building the Next Generation of AI-Native Life Science Companies
The convergence of AI and life sciences represents an unprecedented opportunity for founders and early-stage leaders. Success requires not just technological sophistication, but organizational cultures that bridge disciplinary divides and attract exceptional talent that makes breakthrough discoveries possible.
Reach out today to start building the AI-native biotech leadership team your growth demands.
References
- IQVIA, Global Trends in R&D, 2025
https://www.iqvia.com/insights/the-iqvia-institute/reports-and-publications/reports/global-trends-in-r-and-d-2025
- Stat+ News, Recursion’s incoming CEO: Beyond hype on AI-driven drug development, it’s time to show results. Nov 21, 2025
https://www.statnews.com/2025/11/21/recursion-ai-drug-development-najat-khan/
- Nature, Clinical Trials Gain Confidence, July 15, 2025
https://www.nature.com/articles/s41587-025-02754-1#:~:text=Artificial%20Intelligence%20(AI)%20has%20revolutionized,%25%20(ref.%201).
- Nature, Unlocking the potential: multimodal AI in biotechnology and digital medicine—economic impact and ethical challenges, Oct 20, 2025.
https://www.nature.com/articles/s41746-025-01992-6
- Pitchbook Analyst Note: AI in Drug Development, Nov. 11, 2025.
https://pitchbook.com/news/reports/q4-2025-pitchbook-analyst-note-ai-in-drug-development#downloadReport
- Insitro Extends Research Collaboratin with Bristol Myers Squibb Leveraging insitro’s ChemML Discovery Platform, Oct 14, 2025
https://www.insitro.com/news/insitro-extends-research-collaboration-with-bristol-myers-squibb-leveraging-insitros-chemml-discovery-platform/
- Endpoints, A chemistry startup in Scotland raises over $50M Series B to scale its ‘chemputers’, Oct 21, 2025.
https://endpoints.news/chemify-raises-50m-series-b-to-scale-molecule-making-business/
