Ofer Haviv is the CEO and President of Evogene. Prior to this role, he served as the company’s COO and CFO from 2002 to 2004 and played a key role in Evogene’s spin-off from Compugen in 2002. At Compugen, he held the position of Director of Finance and Treasurer for four years, during which time the company completed two private placements and an IPO on NASDAQ.
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Evogene (NASDAQ:EVGN, TASE: EVGN.TA) is a computational biology company specializing in transforming product discovery and development across various life science industries, including human health and agriculture. The company leverages its versatile Computational Predictive Biology (CPB) platform to drive innovation in these fields.
Since joining as CEO in 2004, you’ve overseen Evogene’s transition from spin-off to a Nasdaq-listed leader in computational biology. What have been the most pivotal moments or decisions that shaped the company’s current direction?
Three strategic decisions shaped Evogene as it is today:
- The decision in 2013 to go public on NASDAQ.
- The decision in 2016 to evolve from a single computational system (CPB) that mainly supported the development of products based on genetic elements for the agricultural industry, into three separate technological engines that combine unique data, computational systems, and a deep understanding of life sciences:
- GeneRator: Supports Evogene’s original activity in the field of products based on a deep understanding of genomics.
- MicroBoost: Directs and accelerates the development of microbe-based products.
- ChemPass: Directs and accelerates the development of chemistry-based products.
- The decision to use these unique technological engines with Evogene’s own researchers to develop products in various fields. This activity, which began as divisions within the company, later became the foundation for building Evogene’s subsidiaries, including:
- Biomica: Uses the MicroBoost technological engine to develop human microbiome-based drugs.
- Lavie Bio: Uses MicroBoost to develop biological products based on microbes for agriculture, protecting plants from pests and improving yields.
- AgPlenus: Uses the ChemPass technological engine to develop chemical products for crop protection against pests.
- Casterra: Uses GeneRator to develop unique castor varieties for cultivating castor plants to produce oil for the growing industries of biological products and alternative fuels.
Computational biology requires top-tier talent in biology, AI, and data science. How does Evogene attract and retain experts in these fields, and what skills or backgrounds do you prioritize?
At Evogene, we attract top talent by fostering a collaborative environment that integrates biology, artificial intelligence, and computational expertise. We value individuals with multidisciplinary experience, particularly those who have worked across diverse fields and bring ‘real-world’ insights. Creativity and problem-solving are at the core of what we seek, enabling our team to tackle complex challenges with innovative solutions.
Being headquartered in Israel—a global leader in high-tech innovation with an ecosystem that fosters agility and forward-thinking— enhances our ability to draw exceptional talent.
Evogene’s proximity to world-class academic institutions, such as the Weizmann Institute, plays a significant role in attracting skilled professionals in biology, AI, and data science.
Evogene offers professionals from the tech world a unique opportunity to apply their expertise in developing products for the life sciences sector—fields that profoundly influence the quality of life and the food we eat. This intersection of technology and life sciences is unlike anything found in traditional high-tech industries. For biologists, we provide advanced technological tools that empower them to realize their product visions at a level unparalleled anywhere else.
Could you elaborate on the core principles behind Evogene’s Computational Predictive Biology (CPB) platform with its AI tech-engines, and how it differentiates from other predictive AI models in life sciences?
Evogene’s Computational Predictive Biology (CPB) platform integrates a deep understanding of biology and chemistry with AI, machine learning, computational models, and biological data to perform analyses across millions of data points. These established AI tech-engines are designed to assist researchers in product discovery, streamline the development of new products, and have been a driving force in our many collaborations.
Our uniqueness can be characterized by three parameters:
- The strong connection between deep knowledge in biology and chemistry and the computational world in the development process of the applications themselves, as well as the flexibility of the applications to adapt to the definitions of different products.
- Our effort to predict, as early as the discovery stage, the likelihood of a candidate successfully meeting the criteria for a commercial product—criteria that are typically examined at much later stages of product development.
- Evogene operates simultaneously in three domains—genomics, chemistry, and microbes—providing a more comprehensive understanding of the development process.
Given the company’s focus on revolutionizing product discovery across health, agriculture, and industrial applications, what are Evogene’s long-term goals for expanding its impact in these sectors?
Our long-term goals can be divided into three:
- Invest in our tech engines for the benefit of existing partners so that we can better predict the correct candidates for validation and can better include additional criteria for product development early on. In short, the continued improvement of our engines.
- To expand the variety of uses for our engines to additional segments not currently addressed by Evogene’s existing subsidiaries, such as our current strategic focus on drug discovery through the ChemPass-AI engine.
- To promote the value of our subsidiaries and benefit as shareholders through the sale of some of our holdings or by receiving dividends.
How has the CPB platform evolved since its inception, and what are some recent advancements or challenges you’ve encountered in developing new tech-engines like ChemPass AI and MicroBoost AI?
The Computational Predictive Biology (CPB) platform was initially developed using a monolithic architecture, integrating a suite of bioinformatics applications primarily focused on plant genomics. Recognizing the need for greater flexibility and scalability, the platform was transitioned to a microservices architecture, enabling significant enhancements to both the User Interface (UI) and User Experience (UX). This architectural evolution has supported the platform’s expansion into new domains within the life sciences, beyond genomics, including microbiology and chemistry, leading to the development of innovative tech-engines such as ChemPass AI for small molecule discovery and MicroBoost AI for microbiome-based applications. While scaling these technologies has presented challenges, the platform’s multidisciplinary approach ensures continued progress and impactful advancements across diverse scientific disciplines.
How did the collaboration with Google Cloud come about, and what were the main factors that made Google Cloud the preferred partner for Evogene?
Our collaboration with Google Cloud was driven by a shared vision of leveraging advanced AI technologies to transform small molecule drug discovery and development. Google Cloud’s robust Vertex AI platform, cutting-edge GPUs, and vast storage capabilities provide the computational power required to train our foundation model on ~40 billion molecular structures. Their expertise in AI and machine learning, combined with Evogene’s strength in computational chemistry, creates a synergy that enables rapid innovation, scalability, and unprecedented diversity in molecular design. This collaboration is accelerating our ability to bring transformative solutions to drug discovery and potentially other life-science products.
The foundation model aims to generate and evaluate novel small molecules. What immediate and long-term impacts do you foresee this having on the speed and accuracy of drug and product development?
The foundation model approach represents a cutting-edge innovation in drug and product development, enabling pre-training on significantly larger datasets than traditional AI-methods. This capability allows for deeper insights and enhanced precision, marking a transformative shift in drug discovery and development. In the short term, the model will revolutionize the discovery stage by rapidly generating novel small molecules with desired pre-defined properties, broadening the chemical diversity by breaking out of the very narrow chemical space explored and uncovering novel, high-potential chemical compounds. Long-term, the integration of AI in the discovery stage can significantly benefit later stages of drug development, potentially even up to clinical stages of development.
How do you anticipate this technology influencing pharmaceutical R&D? What are some of the most pressing challenges in this field that you believe this model can help solve?
Foundation models for small molecule drug discovery hold immense promise for revolutionizing pharmaceutical R&D by significantly cutting down the time and costs of development and increasing probability of success. This technology allows for the rapid and accurate generation of promising drug candidates, potentially reducing the 12-15 year development timeline and the exorbitant costs, often exceeding $2 billion per drug. By streamlining the process and increasing the probability of success in reaching the product commercialization stage, foundation models can promote future innovative therapies and provide better treatment options for patients with life-threatening diseases.
With growing competition in AI for life sciences, how does Evogene plan to maintain a competitive edge in computational biology and molecular design?
Evogene’s competitive edge stems from the expertise of its multidisciplinary team (algorithm developers, software engineers, chemists and biologists), the integration of proprietary algorithms to enhance screening and optimization, and its agility in tailoring solutions to market needs. Our collaboration with Google Cloud plays a pivotal role in advancing our capabilities, leveraging cutting-edge AI tools to refine and accelerate de-novo small molecule design. Flexible collaboration models further ensure our proprietary technologies deliver impactful, market-aligned solutions.
Looking ahead, what is your long-term vision for Evogene’s role in shaping the future of computational biology, and how do you see the company impacting the life sciences industry over the next decade?
Evogene’s vision is to continue being at the forefront of computational biology and chemistry, shaping the future of life sciences product development. Over the next decade, we envision expanding our technological reach through strategic partnerships, driving advancements in human health, agriculture, and sustainability to address critical global challenges. Our ultimate goal is to transform these advancements into innovative products—groundbreaking therapeutics, sustainable agricultural solutions, and eco-friendly technologies.
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