Ofer Haviv is the CEO and President of Evogene. Prior to this role, he served as the corporate’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 4 years, during which period the corporate accomplished 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 corporate 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 essentially the most pivotal moments or decisions that shaped the corporate’s current direction?
Three strategic decisions shaped Evogene because it is today:
- The choice in 2013 to go public on NASDAQ.
- The choice in 2016 to evolve from a single computational system (CPB) that mainly supported the event of products based on genetic elements for the agricultural industry, into three separate technological engines that mix unique data, computational systems, and a deep understanding of life sciences:
- GeneRator: Supports Evogene’s original activity in the sector of products based on a deep understanding of genomics.
- MicroBoost: Directs and accelerates the event of microbe-based products.
- ChemPass: Directs and accelerates the event of chemistry-based products.
- The choice to make use of these unique technological engines with Evogene’s own researchers to develop products in various fields. This activity, which began as divisions throughout the company, later became the muse for constructing 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 supply 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 that have worked across diverse fields and produce ‘real-world’ insights. Creativity and problem-solving are on the core of what we seek, enabling our team to tackle complex challenges with progressive solutions.
Being headquartered in Israel—a worldwide leader in high-tech innovation with an ecosystem that fosters agility and forward-thinking— enhances our ability to attract exceptional talent.
Evogene’s proximity to world-class academic institutions, reminiscent of the Weizmann Institute, plays a big role in attracting expert professionals in biology, AI, and data science.
Evogene offers professionals from the tech world a singular opportunity to use their expertise in developing products for the life sciences sector—fields that profoundly influence the standard of life and the food we eat. This intersection of technology and life sciences is unlike anything present in traditional high-tech industries. For biologists, we offer advanced technological tools that empower them to comprehend 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 the way 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 tens of millions of knowledge points. These established AI tech-engines are designed to help researchers in product discovery, streamline the event of latest products, and have been a driving force in our many collaborations.
Our uniqueness could be characterised by three parameters:
- The strong connection between deep knowledge in biology and chemistry and the computational world in the event technique of the applications themselves, in addition to the pliability of the applications to adapt to the definitions of various products.
- Our effort to predict, as early as the invention stage, the likelihood of a candidate successfully meeting the factors for a industrial product—criteria which might be typically examined at much later stages of product development.
- Evogene operates concurrently in three domains—genomics, chemistry, and microbes—providing a more comprehensive understanding of the event process.
Given the corporate’s give attention to 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 could be divided into three:
- Spend money on our tech engines for the advantage of existing partners in order that we will higher predict the proper candidates for validation and may higher include additional criteria for product development early on. Briefly, the continued improvement of our engines.
- To expand the variability of uses for our engines to additional segments not currently addressed by Evogene’s existing subsidiaries, reminiscent of our current strategic give attention to drug discovery through the ChemPass-AI engine.
- To advertise the worth of our subsidiaries and profit as shareholders through the sale of a few 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 latest tech-engines like ChemPass AI and MicroBoost AI?
The Computational Predictive Biology (CPB) platform was initially developed using a monolithic architecture, integrating a collection of bioinformatics applications primarily focused on plant genomics. Recognizing the necessity for greater flexibility and scalability, the platform was transitioned to a microservices architecture, enabling significant enhancements to each the User Interface (UI) and User Experience (UX). This architectural evolution has supported the platform’s expansion into latest domains throughout the life sciences, beyond genomics, including microbiology and chemistry, resulting in the event of progressive tech-engines reminiscent of 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 most important aspects that made Google Cloud the popular partner for Evogene?
Our collaboration with Google Cloud was driven by a shared vision of leveraging advanced AI technologies to remodel 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 coach 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 allows 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 inspiration model goals 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 inspiration 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. Within the short term, the model will revolutionize the invention 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 mixing of AI in the invention stage can significantly profit later stages of drug development, potentially even as much as clinical stages of development.
How do you anticipate this technology influencing pharmaceutical R&D? What are among the most pressing challenges on this field that you simply imagine this model will 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 yr development timeline and the exorbitant costs, often exceeding $2 billion per drug. By streamlining the method and increasing the probability of success in reaching the product commercialization stage, foundation models can promote future progressive therapies and supply higher treatment options for patients with life-threatening diseases.
With growing competition in AI for all times sciences, how does Evogene plan to take care of 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 mixing of proprietary algorithms to reinforce 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 speed up de-novo small molecule design. Flexible collaboration models further ensure our proprietary technologies deliver impactful, market-aligned solutions.
Looking ahead, what’s your long-term vision for Evogene’s role in shaping the longer term of computational biology, and the way do you see the corporate impacting the life sciences industry over the subsequent decade?
Evogene’s vision is to proceed being on the forefront of computational biology and chemistry, shaping the longer term of life sciences product development. Over the subsequent decade, we envision expanding our technological reach through strategic partnerships, driving advancements in human health, agriculture, and sustainability to handle critical global challenges. Our ultimate goal is to remodel these advancements into progressive products—groundbreaking therapeutics, sustainable agricultural solutions, and eco-friendly technologies.
