Ofer Haviv is the CEO and President of Evogene. Previous to this function, he served as the corporate’s COO and CFO from 2002 to 2004 and performed a key function in Evogene’s spin-off from Compugen in 2002. At Compugen, he held the place of Director of Finance and Treasurer for 4 years, throughout which period the corporate accomplished two non-public placements and an IPO on NASDAQ.
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Evogene (NASDAQ:EVGN, TASE: EVGN.TA) is a computational biology firm specializing in remodeling product discovery and growth throughout numerous life science industries, together with human well being and agriculture. The corporate leverages its versatile Computational Predictive Biology (CPB) platform to drive innovation in these fields.
Since becoming a member of as CEO in 2004, you’ve overseen Evogene’s transition from spin-off to a Nasdaq-listed chief in computational biology. What have been essentially the most pivotal moments or selections that formed the corporate’s present route?
Three strategic selections formed Evogene as it’s immediately:
- The choice in 2013 to go public on NASDAQ.
- The choice in 2016 to evolve from a single computational system (CPB) that primarily supported the event of merchandise based mostly on genetic parts for the agricultural business, into three separate technological engines that mix distinctive knowledge, computational programs, and a deep understanding of life sciences:
- GeneRator: Helps Evogene’s unique exercise within the area of merchandise based mostly on a deep understanding of genomics.
- MicroBoost: Directs and accelerates the event of microbe-based merchandise.
- ChemPass: Directs and accelerates the event of chemistry-based merchandise.
- The choice to make use of these distinctive technological engines with Evogene’s personal researchers to develop merchandise in numerous fields. This exercise, which started as divisions throughout the firm, later turned the inspiration for constructing Evogene’s subsidiaries, together with:
- Biomica: Makes use of the MicroBoost technological engine to develop human microbiome-based medicine.
- Lavie Bio: Makes use of MicroBoost to develop organic merchandise based mostly on microbes for agriculture, defending crops from pests and enhancing yields.
- AgPlenus: Makes use of the ChemPass technological engine to develop chemical merchandise for crop safety towards pests.
- Casterra: Makes use of GeneRator to develop distinctive castor varieties for cultivating castor crops to provide oil for the rising industries of organic merchandise and different fuels.
Computational biology requires top-tier expertise in biology, AI, and knowledge science. How does Evogene entice and retain specialists in these fields, and what abilities or backgrounds do you prioritize?
At Evogene, we entice prime expertise by fostering a collaborative atmosphere that integrates biology, synthetic intelligence, and computational experience. We worth people with multidisciplinary expertise, significantly those that have labored throughout numerous fields and convey ‘real-world’ insights. Creativity and problem-solving are on the core of what we search, enabling our crew to sort out advanced challenges with modern options.
Being headquartered in Israel—a world chief in high-tech innovation with an ecosystem that fosters agility and forward-thinking— enhances our skill to attract distinctive expertise.
Evogene’s proximity to world-class educational establishments, such because the Weizmann Institute, performs a major function in attracting expert professionals in biology, AI, and knowledge science.
Evogene provides professionals from the tech world a singular alternative to use their experience in growing merchandise for the life sciences sector—fields that profoundly affect the standard of life and the meals we eat. This intersection of know-how and life sciences is not like something present in conventional high-tech industries. For biologists, we offer superior technological instruments that empower them to appreciate their product visions at a degree unparalleled anyplace else.
May you elaborate on the core rules behind Evogene’s Computational Predictive Biology (CPB) platform with its AI tech-engines, and the way it differentiates from different predictive AI fashions in life sciences?
Evogene’s Computational Predictive Biology (CPB) platform integrates a deep understanding of biology and chemistry with AI, machine studying, computational fashions, and organic knowledge to carry out analyses throughout tens of millions of information factors. These established AI tech-engines are designed to help researchers in product discovery, streamline the event of latest merchandise, and have been a driving power in our many collaborations.
Our uniqueness could be characterised by three parameters:
- The sturdy connection between deep information in biology and chemistry and the computational world within the growth technique of the functions themselves, in addition to the pliability of the functions to adapt to the definitions of various merchandise.
- Our effort to foretell, as early as the invention stage, the chance of a candidate efficiently assembly the standards for a industrial product—standards which might be sometimes examined at a lot later levels of product growth.
- Evogene operates concurrently in three domains—genomics, chemistry, and microbes—offering a extra complete understanding of the event course of.
Given the corporate’s deal with revolutionizing product discovery throughout well being, agriculture, and industrial functions, what are Evogene’s long-term objectives for increasing its influence in these sectors?
Our long-term objectives could be divided into three:
- Put money into our tech engines for the good thing about current companions in order that we will higher predict the proper candidates for validation and might higher embody further standards for product growth early on. Briefly, the continued enchancment of our engines.
- To develop the number of makes use of for our engines to further segments not at the moment addressed by Evogene’s current subsidiaries, reminiscent of our present strategic deal with drug discovery via the ChemPass-AI engine.
- To advertise the worth of our subsidiaries and profit as shareholders via the sale of a few of our holdings or by receiving dividends.
How has the CPB platform developed since its inception, and what are some current developments or challenges you’ve encountered in growing new tech-engines like ChemPass AI and MicroBoost AI?
The Computational Predictive Biology (CPB) platform was initially developed utilizing a monolithic structure, integrating a collection of bioinformatics functions primarily centered on plant genomics. Recognizing the necessity for larger flexibility and scalability, the platform was transitioned to a microservices structure, enabling important enhancements to each the Consumer Interface (UI) and Consumer Expertise (UX). This architectural evolution has supported the platform’s growth into new domains throughout the life sciences, past genomics, together with microbiology and chemistry, resulting in the event of modern tech-engines reminiscent of ChemPass AI for small molecule discovery and MicroBoost AI for microbiome-based functions. Whereas scaling these applied sciences has introduced challenges, the platform’s multidisciplinary method ensures continued progress and impactful developments throughout numerous scientific disciplines.
How did the collaboration with Google Cloud come about, and what have been the principle elements that made Google Cloud the popular companion for Evogene?
Our collaboration with Google Cloud was pushed by a shared imaginative and prescient of leveraging superior AI applied sciences to remodel small molecule drug discovery and growth. Google Cloud’s sturdy Vertex AI platform, cutting-edge GPUs, and huge storage capabilities present the computational energy required to coach our basis mannequin on ~40 billion molecular buildings. Their experience in AI and machine studying, mixed with Evogene’s energy in computational chemistry, creates a synergy that allows speedy innovation, scalability, and unprecedented range in molecular design. This collaboration is accelerating our skill to deliver transformative options to drug discovery and doubtlessly different life-science merchandise.
The muse mannequin goals to generate and consider novel small molecules. What instant and long-term impacts do you foresee this having on the pace and accuracy of drug and product growth?
The muse mannequin method represents a cutting-edge innovation in drug and product growth, enabling pre-training on considerably bigger datasets than conventional AI-methods. This functionality permits for deeper insights and enhanced precision, marking a transformative shift in drug discovery and growth. Within the brief time period, the mannequin will revolutionize the invention stage by quickly producing novel small molecules with desired pre-defined properties, broadening the chemical range by breaking out of the very slim chemical area explored and uncovering novel, high-potential chemical compounds. Lengthy-term, the mixing of AI within the discovery stage can considerably profit later levels of drug growth, doubtlessly even as much as scientific levels of growth.
How do you anticipate this know-how influencing pharmaceutical R&D? What are among the most urgent challenges on this area that you just imagine this mannequin will help remedy?
Basis fashions for small molecule drug discovery maintain immense promise for revolutionizing pharmaceutical R&D by considerably reducing down the time and prices of growth and growing chance of success. This know-how permits for the speedy and correct era of promising drug candidates, doubtlessly decreasing the 12-15 12 months growth timeline and the exorbitant prices, typically exceeding $2 billion per drug. By streamlining the method and growing the chance of success in reaching the product commercialization stage, basis fashions can promote future modern therapies and supply higher therapy choices for sufferers with life-threatening ailments.
With rising competitors in AI for all times sciences, how does Evogene plan to take care of a aggressive edge in computational biology and molecular design?
Evogene’s aggressive edge stems from the experience of its multidisciplinary crew (algorithm builders, software program engineers, chemists and biologists), the mixing of proprietary algorithms to reinforce screening and optimization, and its agility in tailoring options to market wants. Our collaboration with Google Cloud performs a pivotal function in advancing our capabilities, leveraging cutting-edge AI instruments to refine and speed up de-novo small molecule design. Versatile collaboration fashions additional guarantee our proprietary applied sciences ship impactful, market-aligned options.
Wanting forward, what’s your long-term imaginative and prescient for Evogene’s function in shaping the way forward for computational biology, and the way do you see the corporate impacting the life sciences business over the following decade?
Evogene’s imaginative and prescient is to proceed being on the forefront of computational biology and chemistry, shaping the way forward for life sciences product growth. Over the following decade, we envision increasing our technological attain via strategic partnerships, driving developments in human well being, agriculture, and sustainability to deal with crucial international challenges. Our final objective is to remodel these developments into modern merchandise—groundbreaking therapeutics, sustainable agricultural options, and eco-friendly applied sciences.