The biotechnology investment landscape has fundamentally changed. Traditional drug discovery timelines that stretch 8-10 years from target identification to human trials are being compressed by artificial intelligence applications that can analyze massive datasets in days rather than decades.
Leen Kawas, Managing General Partner at Propel Bio Partners, has witnessed this transformation from multiple angles. As a biotech CEO, she saw firsthand how AI could accelerate drug development. Now, as an investor, she’s actively funding the next generation of AI-driven life sciences companies.
“We see a big surge in the number of companies that are trying to use AI and predictive modeling to accelerate drug development and discovery,” Leen Kawas observed in early 2023. For venture capitalists, this surge represents both unprecedented opportunity and new evaluation challenges. The question isn’t whether AI will transform life sciences—it’s how investors can identify which AI applications will deliver breakthrough results.
The AI Advantage in Drug Discovery
The traditional pharmaceutical development process follows predictable timelines: three to five years for target and drug discovery-led optimization, including animal testing, followed by another three to five years of human trials. These extended timelines create enormous capital requirements and limited opportunities for venture returns.
AI-based startups promise to fundamentally alter this equation. Leen Kawas explains that AI holds the potential to “reduce timelines for drug discovery, improve predictions on clinical efficacy and safety, and can diversify drug pipelines without any bias from individual experience.”
For investors, this timeline compression represents a strategic opportunity. Companies that can reduce development timelines while improving success rates offer the potential for both faster returns and lower overall investment requirements. But identifying which AI applications will deliver these benefits requires sophisticated evaluation frameworks.
The key insight from Leen Kawas’s perspective is that AI’s value lies not just in computational power, but in its ability to process diverse datasets that would be impossible for human researchers to analyze comprehensively. “AI enables us to bring a number of different data (like omics, metabolomics, proteomics, epigenetics, and clinical presentation) to empower more accurate and comprehensive decision-making,” she notes.
Portfolio Strategy: Identifying High-Impact AI Applications
Leen Kawas’s investment approach at Propel Bio Partners provides a framework for how VCs can build AI-focused life sciences portfolios. Rather than investing in AI technology for its own sake, she focuses on companies using AI to solve specific healthcare problems with measurable patient impact.
Her portfolio company Persephone Biosciences exemplifies this strategic approach. The company “uses AI and machine learning to discover patient datasets’ biomarkers” but does so within a broader platform that addresses real clinical needs. As Leen Kawas explains, “Persephone’s technology platform is based on diverse and inclusive, population-scale, observational clinical trials in conjunction with advanced multi-omics analyses and machine learning.”
This integration approach offers several investment advantages. First, it reduces technology risk by combining AI capabilities with proven clinical methodologies. Second, it creates multiple value creation pathways—both through technological advancement and clinical validation. Third, it provides clear metrics for measuring progress through patient outcomes rather than just computational benchmarks.
Inherent Biosciences represents another strategic application of AI investment. The company “uses machine learning to identify epigenetic biomarkers (epimutations) for use in diagnostics and potential therapeutic targets.” Their sperm vitality calculator, which “integrates sperm DNA methylation signatures and is highly predictive of an individual’s biological age,” demonstrates how AI can create entirely new diagnostic categories.
For VCs building AI-focused portfolios, these examples suggest prioritizing companies that use AI to enable new capabilities rather than just optimize existing processes. The most valuable AI applications often create previously impossible solutions rather than marginally improving current approaches.
Due Diligence in the AI Era
Traditional biotech due diligence focuses heavily on scientific validation, regulatory pathways, and market potential. AI-driven companies require additional evaluation criteria that many VCs haven’t yet developed.
Leen Kawas’s experience suggests that investors should focus particularly on data quality and diversity. The most successful AI applications in life sciences depend on access to large, diverse, high-quality datasets. Companies with unique data assets or superior data collection capabilities often have sustainable competitive advantages that pure algorithmic improvements can’t replicate.
Persephone Biosciences’ emphasis on “diverse and inclusive, population-scale, observational clinical trials” illustrates this principle. Their competitive advantage lies not just in their machine learning algorithms, but in their ability to collect representative data that produces broadly applicable results.
Data diversity becomes particularly important in life sciences because patient populations vary significantly across genetic backgrounds, environmental factors, and disease presentations. AI models trained on homogeneous datasets often fail when applied to broader populations, creating both regulatory challenges and commercial limitations.
Investors should also evaluate how companies handle data integration across multiple sources. Leen Kawas notes that AI’s power comes from combining “omics, metabolomics, proteomics, epigenetics, and clinical presentation” data. Companies that can successfully integrate these diverse data types often achieve better predictive accuracy than those focusing on single data sources.
Clinical Translation: Where AI Meets Reality
The ultimate test of AI applications in life sciences is clinical translation—the ability to improve patient outcomes in real-world settings. Leen Kawas’s experience reveals that the most successful AI companies focus on this translation from the beginning rather than treating it as an afterthought.
AI technology can “analyze a blood sample, obtained by a clinical trial participant, capture a large volume of information, and potentially correlate it to efficacy or safety endpoints,” she explains. This capability enables more efficient clinical trials through better patient selection, more sensitive outcome measurements, and faster safety signal detection.
For investors, companies with clear paths to clinical validation often represent better investment opportunities than those focused purely on technological capabilities. The regulatory approval process provides natural milestones for measuring progress and reduces the risk of developing impressive technology that can’t be commercialized.
The personalized medicine applications that Kawas emphasizes represent particularly attractive investment opportunities. “Technology can lead to better tools for individualized and precision medicine. It allows us to make sense of the different factors that can make each individual or patient unique,” she notes.
These personalized approaches often command premium pricing and face less direct competition than broad-spectrum treatments. They also align with regulatory trends toward precision medicine and value-based care models.
Market Timing and Competitive Dynamics
Leen Kawas’s observation about the “big surge” in AI-driven drug development companies reflects broader market dynamics that create both opportunities and challenges for investors. While increased activity validates the market potential, it also intensifies competition for the best deals and talent.
Her investment philosophy suggests focusing on companies with sustainable competitive advantages rather than those relying solely on first-mover benefits. “Using AI to have a holistic view of patients and individuals can lead to the discovery of new therapies or technologies that can help humans live healthier and better,” she explains.
This holistic approach often requires capabilities that are difficult to replicate quickly. Companies that combine AI expertise with deep clinical knowledge, unique data assets, and established regulatory relationships create more defensible market positions than pure technology plays.
The rapid advancement in computational power and data sciences that Leen Kawas identifies as driving AI adoption also creates ongoing opportunities for new breakthroughs. Investors who understand these technological trends can identify companies positioned to benefit from future capabilities rather than just current applications.
Risk Management in AI Investments
AI applications in life sciences face unique risks that require specialized evaluation approaches. Unlike traditional software applications where failures create limited downside, healthcare AI mistakes can have serious patient safety implications and regulatory consequences.
Leen Kawas’s patient-centric investment philosophy provides a framework for managing these risks. Companies that prioritize patient outcomes and safety from the beginning often develop more robust technologies and face fewer regulatory obstacles than those focused primarily on technological performance.
Investors should also consider the interpretability requirements for healthcare AI applications. While black-box algorithms may achieve impressive performance metrics, regulatory agencies and clinicians often require explainable AI systems that can provide clear rationales for their recommendations.
The diversity emphasis in Leen Kawas’s portfolio companies also reflects important risk management considerations. AI systems trained on diverse datasets are more likely to perform well across different patient populations and less likely to exhibit harmful biases that could create regulatory or commercial problems.
The Future of AI-Driven Life Sciences Investing
Looking ahead, Leen Kawas’s perspective suggests that AI will become increasingly integrated into all aspects of life sciences rather than remaining a separate technology category. “AI enables improved diagnostic performance” and “brings personalized patient treatment into focus” while also helping with “chronic disease identification and treatment.”
For investors, this integration trend suggests that AI evaluation capabilities will become essential for all life sciences investing rather than just specialized AI funds. Understanding how AI enhances traditional biotech business models will become as important as evaluating pure AI companies.
The convergence of AI with other emerging technologies—including synthetic biology, advanced manufacturing, and digital therapeutics—will create new investment categories that require hybrid evaluation approaches combining multiple technical disciplines.
The Competitive Advantage
VCs who develop sophisticated AI evaluation capabilities now will have significant advantages as the technology becomes more pervasive in life sciences. Leen Kawas’s success in identifying and funding AI-driven companies like Persephone Biosciences and Inherent Biosciences demonstrates how understanding both AI capabilities and life sciences applications creates superior investment outcomes.
The key insight is that successful AI investing in life sciences requires deep understanding of both domains. Technology expertise alone is insufficient for evaluating clinical applications, while life sciences knowledge without AI understanding misses crucial competitive dynamics.
As Leen Kawas concludes, the goal is supporting companies that use AI to help “humans live healthier and better.” For investors, this patient-centric focus provides both a moral compass and a practical framework for identifying the AI applications most likely to achieve sustainable commercial success in life sciences.
