Five Women Shaping the AI–Life Science Stack

February 11 marks the UN-designated International Day of Women and Girls in Science, established by the General Assembly in December 2015 to recognize the contributions of women in scientific fields and to encourage fuller participation across education, research, and leadership. The resolution calls on governments and UN bodies to widen access to science education, jobs and decision-making, to tackle legal and social barriers that keep women out, and to make their scientific work visible within the 2030 goals on education, gender equality and innovation.

This article is brought to you by our writer Anastasiia Rohozianska; also, check out our regular BiopharmaTrend.com contributor, Dr. Louise von Stechow, who writes on AI, biotech strategy, rare disease therapeutics, and emerging modalities.

Each year, the day has a different theme. This year it’s “Synergizing AI, Social Science, STEM and Finance: Building Inclusive Futures for Women and Girls,” which makes it natural to first ask where women generally stand in science, AI and finance today before looking at those who are now shaping these systems.

The numbers show that progress has been slow: women still account for roughly one-third of researchers worldwide and about 35% of STEM graduates, a proportion UNESCO data indicate has barely shifted in a decade. Inside the AI pillar that is rapidly reshaping drug discovery, healthcare and finance, women are estimated to hold around a quarter of AI jobs and less than 15% of senior roles, while a global synthesis of usage studies finds women about 20% less likely than men to engage with generative AI tools in the first place. At the same time, a UN analyses suggest a higher share of women’s jobs than men’s are exposed to AI-enabled automation, particularly in clerical and administrative roles, making it more likely that women experience AI as something that reshapes or displaces their work rather than an arena where they set agendas and build systems.

At BiopharmaTrend, we cover the intersection of advanced biology, digital technologies, and emerging therapeutics—so for this occasion, we focus on STEM through the lens of life sciences.

Today, genomics, cell signalling, neurotrophic pathways and infectious-disease therapeutics are standard components of drug discovery pipelines, but much of this infrastructure traces back to scientists whose careers were shaped by structural barriers and delayed recognition, which is why an international observance focused on women and girls in science is not just symbolic context for an AI-era discussion, but part of the story: modern biomedicine is built on work that women often completed without full recognition.

• Rosalind Franklin’s X-ray crystallography on DNA and viruses was central to solving the double helix and later structural virology, but her role was widely recognized only decades later. 
• Barbara McClintock’s maize genetics revealed transposable elements and genes as mobile (“jumping genes”), now fundamental to genomics and epigenetics, yet her Nobel Prize came more than 30 years after her first reports. 
• Working under fascist racial laws that barred Jewish scientists from universities, Rita Levi-Montalcini identified nerve growth factor, the first growth factor and a basis of modern cell and neurobiology. 
• Tu Youyou’s isolation of artemisinin created a new class of antimalarial drugs that have saved millions of lives and remain standard malaria treatment, while her contribution stayed largely unknown outside China for many years.

Below, we turn to women who are defining research, product, and investment priorities across AI and life sciences, rather than appearing only as those whose jobs, data, or care are shaped by these systems.

Daphne Koller — Founder & CEO at insitro

Daphne Koller, PhD, is a computer scientist and entrepreneur who moved from foundational work in probabilistic graphical models and Bayesian machine learning at Stanford into building an AI-native drug discovery company as founder and CEO of insitro. Koller’s academic career spans key contributions at the interface of AI, computer vision, and computational biology, recognized with a MacArthur Fellowship, the ACM Prize in Computing, and election to the US National Academy of Engineering and National Academy of Sciences. She also co‑founded Coursera and later co‑founded Engageli; today she remains engaged in digital learning while focusing her primary efforts on applied biology and therapeutics at insitro.

At insitro Dr. Koller oversees a platform that couples high‑throughput human cellular systems with machine learning models optimized for target discovery and molecule design. The insitro Human (ISH) platform builds iPSC‑derived disease models using human genetics and functional genomics, while complementary systems such as the POSH (Pooled Optical Screening in Human cells) platform combine pooled CRISPR perturbations, high‑content imaging and self‑supervised deep learning; together these platforms generate multimodal, multi‑omics datasets that feed cell‑level ML models. 

ChemML, insitro’s small‑molecule design engine, integrates proprietary binding and ADMET data with physics‑based in silico screening, DNA‑encoded libraries and active‑learning medicinal chemistry to design and optimize small‑molecule therapeutics. With the acquisition of CombinAbleAI and the launch of the TherML platform, insitro now operates a single AI system that spans small molecules, oligonucleotides, antibodies and other complex biologics, explicitly optimizing for efficacy, developability and safety.

insitro’s preclinical portfolio is backed by major pharma alliances and in-house metabolic programs, with milestones including a novel ALS target from a Bristol Myers Squibb partnership, a Gilead deal in NASH worth up to $200M per target, and an AI-driven metabolic disease discovery program feeding internal efforts and a collaboration with Eli Lilly.

Alice Zhang – Founder & CEO at Verge Genomics 

Alice Zhang is the CEO and co-founder of Verge Genomics, an AI-enabled drug discovery company that uses human patient data to develop drugs for ALS, Parkinson’s, Alzheimer’s and related diseases. She trained in molecular biology at Princeton and spent five years in the UCLA-Caltech MD/PhD program before leaving to start Verge, and she has been recognized on Forbes 30 Under 30 and MIT Technology Review’s Innovators Under 35 lists while serving on the board of the California Life Sciences Association and remaining active as an angel investor in tech-enabled bio companies.

Under Zhang’s leadership, Verge has built CONVERGE, a proprietary “all-in-human” discovery platform that combines one of the field’s larger multi-omic patient tissue datasets with machine learning to find and validate targets directly in human disease biology.

Public description cites more than 61 terabytes of integrated human data supporting an end-to-end platform from target discovery through early clinical testing. The stack includes patient-derived CNS omics, computational target-prioritization models with reported higher validation rates, and digital biomarker tools that capture mobility, respiratory, sleep, and speech data via in-home sensors and AI-based analysis. 

On the capital side, Zhang has led Verge through an oversubscribed $98M Series B led by BlackRock with Eli Lilly, Merck GHI, and others. The company also signed a three-year collaboration with Eli Lilly around up to four ALS targets with up to $694M in milestones, and a four-year, multi-target rare neurodegenerative and neuromuscular disease collaboration with Alexion/AstraZeneca that includes up to roughly $840M in milestones and royalties, alongside an Alexion equity stake.

Suchi Saria – Bayesian Health founder & director of the Machine Learning and Healthcare Lab at Johns Hopkins

Suchi Saria is a computer scientist and AI researcher at Johns Hopkins, where she leads the Machine Learning, AI & Healthcare Lab and founded Bayesian Health, a clinical AI company. Her roles span computer science, medicine, and health policy. Trained at Mount Holyoke and Stanford (PhD under insitro’s Daphne Koller), she’s recognized for work in machine learning and computational healthcare, with honors including a Sloan Fellowship, MIT’s Innovators Under 35, and WEF’s Young Global Leader.

Dr. Saria’s core platform combines methods, data, and governance, with her Johns Hopkins lab developing AI systems that learn from noisy EHR data to drive diagnostic and treatment tools. This work spans theory, deployment, and policy, and is commercialized through Bayesian Health, founded in 2018, as an adaptive clinical AI platform integrated into hospital systems. The research and product have drawn support from agencies like NSF, DARPA, FDA, NIH, and CDC, providing access to large datasets and regulatory-facing initiatives.

Saria also co-founded the Coalition for Health AI (CHAI), bringing together over 1,500 stakeholders, including Mayo Clinic, CVS Health, major cloud providers, the FDA, and the White House OSTP, to establish standards for evaluating healthcare AI. She also serves on the National Academy of Medicine's AI Code of Conduct working group and the editorial board of the Journal of Machine Learning Research.

Saria co-authored TREWScore and TREWS sepsis models, which use real-time ICU data to identify septic shock risk hours before organ failure. TREWS, deployed via Bayesian Health’s platform, was evaluated in a large multi-site Nature Medicine study covering over 760,000 patient encounters, showing an 18.2% relative drop in sepsis mortality. Since its 2023 rollout, Johns Hopkins reports a similar 18% mortality reduction across dozens of hospitals, faster diagnoses by nearly two hours, and significant platform expansion. The same system is now used for other conditions like clinical deterioration and pressure injuries.

Together with Daphne Koller and Anna Penn at Stanford, Saria co-developed PhysiScore, a predictive tool that uses clinical data to assess health risks in premature infants. Beyond her academic and clinical work, she also served as an investment partner at AIX Ventures, where she evaluated and supported early-stage AI startups.

Regina Barzilay — MIT Computer Science & Artificial Intelligence Laboratory (CSAIL) professor & Jameel Clinic faculty lead, Phare Bio scientific advisor

Regina Barzilay is a School of Engineering Distinguished Professor for AI and Health in MIT’s Department of Electrical Engineering and Computer Science, a core member of CSAIL, and the AI faculty lead for the MIT Jameel Clinic for Machine Learning in Health, where she focuses on machine learning for clinical decision support and drug discovery. 

With a background in natural language processing, she is a prominent figure in applying deep learning to oncology and chemistry, recognized with a MacArthur Fellowship, the AAAI Squirrel AI Award, and election to the US National Academies of Engineering and Medicine and the American Academy of Arts and Sciences.

Dr. Barzilay’s group leads some of the most widely cited imaging-based cancer risk models. Mirai is a deep learning system that reads screening mammograms and produces a personalized risk score for up to five years, designed to work across different scanner types and to handle missing clinical covariates. According to the Jameel Clinic, Mirai has now been validated on >1.5 million mammograms across 43 hospitals in 14 countries, and is being deployed through an international hospital network backed by Wellcome Trust. 

Sybil, a companion model for lung cancer screening, predicts six-year risk from a single low-dose CT scan, with performance detailed in the Journal of Clinical Oncology. It forms part of a broader imaging model stack built atop radiology infrastructure and informs screening policy through the Jameel Clinic’s AI Hospital Network.

Barzilay co-leads the Jameel Clinic’s AI antibiotics program with Jim Collins, where deep learning models trained on assay data are used to screen large chemical libraries. Their Cell study identified halicin, a novel antibiotic active against drug-resistant pathogens in mouse models. She also advises Phare Bio, a non‑profit company spun out of the Barzilay/Collins antibiotics work, and is a senior co-author on the open-source Boltz models for therapeutic design, including BoltzGen, which generates protein binders for challenging targets.

Jen Asher — Founder & CEO at 1910 

Jen Asher (Nwankwo) is the founder and CEO of 1910, an AI-native biotech company in Boston  focused on small and large molecule drug discovery using multimodal data, high-throughput lab automation, and advanced AI models.

Dr. Asher holds a PhD in Pharmacology and Experimental Therapeutics from Tufts, founded 1910 Genetics to reflect a molecular-level approach to disease, inspired by the year sickle cell was first identified in the U.S. Before launching the company, she held roles in preclinical drug discovery at Eli Lilly and Novartis, worked in management consulting at Bain, and led business development at a health-tech startup and Transparency Life Sciences.

She leads 1910’s multimodal, multi-AI agent system Input-Transform-Output (ITO) platform, which combines proprietary data sources like simulations and lab assays with automated labs and secure learning across sites. The platform supports both small-molecule and biologics R&D and includes models like CANDID-CNS, which reportedly outperforms industry benchmarks for predicting brain-blood barrier permeability, and PEGASUS, which helps design cell-permeable macrocyclic peptides (a class of ring-shaped molecules that can slip into cells and bind broad, hard-to-drug protein surfaces) by integrating massive assay data with physics-based simulations.

1910 Genetics has a five-year commercial agreement with Microsoft to integrate its ITO platform into Azure Quantum Elements, offering co-discovery, co-engineering, and platform-as-a-service models to global pharma and biotech partners. 

Accenture, now a strategic investor, is co-packaging the platform as an enterprise AI layer for biopharma R&D. The company has raised $26 million in seed and Series A funding from M12 (Microsoft’s venture fund), Playground Global, Sam Altman, FoundersX, and others, with additional strategic capital implied through the Accenture deal.

The five women spotlighted in this piece are only a small part of a much broader growing cohort shaping how AI enters biology and medicine. 

• At ETH Zurich, bioethicist Dr. Effy Vayena is an important voice on digital health and data governance, including co-chairing the WHO expert group on ethics and governance of AI for health. 
• At MIT, Dr. Marzyeh Ghassemi’s Healthy ML group builds machine-learning systems for healthcare that are robust, private and fair, tackling distribution shifts and bias in real clinical data. 
• At UC Berkeley, Dr. Emma Pierson, core faculty in the Computational Precision Health program, develops data-science and machine-learning methods to study inequality and healthcare.
• Harvard’s Dr. Finale Doshi-Velez leads the Data to Actionable Knowledge lab on human-AI decision making, accountability and regulation. 
• At EPFL, Dr. Charlotte Bunne works at the interface of machine learning and cell biology, including co-authoring Cell roadmap on AI-based virtual cell models.
• At Recursion, recent CEO Dr. Najat Khan leads an AI-native biopharma stack built around the Recursion OS platform, which uses large-scale biological and chemical datasets to support a partnered and internal pipeline in oncology, rare diseases and neuroscience, with work also spanning immune-mediated indications.

Together with founders like Daphne Koller, Alice Zhang and Jen Asher, and researchers such as Regina Barzilay, they point to an emerging architecture in which women could be at the center of aligning AI, social science and biomedicine with real-world impact.

UNESCO’s “Closing the Gender Gap in Science” Call to Action, launched in 2024, was explicit about how much work remains. That is why pipeline initiatives targeting girls and young women in AI, coding and computational thinking are more than feel-good side stories. Programs such as Girls Who Code, Black Girls Code, AI4ALL and Technovation Girls already reach large global cohorts of girls, especially from underrepresented communities, combining coding and AI content with entrepreneurship, mentoring and problem solving around real-world challenges. 

From Franklin, McClintock, Tu Youyou, and Levi-Montalcini to today’s AI-native founders, ethicists and methodologists, the story of biology has been repeatedly rewritten by women working against structural headwinds; the International Day of Women and Girls in Science is the yearly checkpoint on whether this next wave of AI-enabled biology will finally be different in who designs it, who funds it and who is allowed to benefit.

Topic: AI in Bio