Though AI's raw potential to transform life science outcomes is now broadly understood, relatively few people possess the capacity to recognize, optimally accommodate, and exploit its deep learning and generative modalities. Quite early in his own career, Dr. Werner Lanthaler had already concluded that real progress in drug research should be primarily informed and driven by a science-focused approach. This pragmatic belief has left him well-placed as a thought leader, offering fresh insights about how to leverage best the power and potential of AI to enhance drug development and reduce costs in the biotech industry. Lanthaler's vision is founded upon the conviction that scientific innovation is here to stay. Scientific progress and breakthroughs may often seem cyclical in nature, with many peaks and troughs along the way. Nevertheless, as Lanthaler observes, every winter is inevitably followed by a spring, so choosing the right science at the right time is always the surest pathway to success.
Werner Lanthaler's Vision: AI as a Catalyst in Drug Development
Werner Lanthaler considers AI and machine learning a powerful tool to support a super-efficient collaborative approach to drug discovery and development. According to this collective model, market-leading pharmaceutical and biotech companies should work shoulder to shoulder-with venture capital investors, the academic community, and PAGs (patient advocacy groups) to accelerate drug research and actively transform the process itself in terms of quality, cost, and timelines.
To reap the benefits of such a cooperative landscape, sharing should not be an option but a core essential. Dr. Lanthaler believes there is little justification for biotech firms working alone—for example, to reinvent processing methodologies that the broader tech industry developed perhaps five years ago. Creating meaningful synergies between commercial companies naturally requires an element of mutual trust, but as the 26 companies that have united to produce COVID-inspired vaccines and treatments have recently demonstrated, the beneficial outcomes can be immense when genuine partner specialists agree to pool their expertise.
Collaborative Models in Biotech: Lanthaler's Call for Industry Cooperation
Lanthaler advises any would-be participants with commercial reservations about collective endeavors to put aside any notions about the perceived economic downsides of networking and look carefully at the real context: Medical drugs are a global market, so any biotech company with a purely domestic outlook and corporate culture will be hampered by a flawed strategy from the outset. Furthermore, with more than 3,300 known diseases currently without an effective treatment or cure, there is a huge untapped potential to richly reward those biotech firms prepared to adopt a collaborative mindset.
While Lanthaler's concept of strategic alliances across the biotech industry has its own virtues, the deployment of AI is the crucial feature that has provided the means to effect a step change. For Lanthaler, AI represents a "data-driven, multi-modal autobahn to cure." AI's much-enhanced capacity for complex data analysis, he argues, makes the R&D process both faster and more cost-efficient, thus creating substantial headroom for augmentation—more scientists doing more experiments and thus furnishing even greater volumes of data to inform their research. Well-targeted research also adds significant value, bringing companies more success, improving profit margins, and thus encouraging more investors to make additional research funding available.
Werner Lanthaler on AI: Transforming Data into Drug Development Insights
During Dr. Lanthaler's time as CEO of Evotec and now at Wlan Holding GmbH, AI has always been an integral component of drug research and not just a bolt-on, software-oriented application. An Evotec-developed tool called PanHunter provides an excellent practical example of Lanthaler's working principles of targeted collaboration and innovation deployed to streamline the drug discovery process. This web-based platform offers multi-omics researchers a flexible software tool that facilitates in-depth data analysis and has the capacity to correlate immense volumes of data. Users find PanHunter responsive and highly interactive. Furthermore, being accessible online, this dedicated solution facilitates collective projects, can be rapidly updated, and is the ideal platform for sharing data and outcomes.
Elsewhere, Evotec's Abacus software suite functions as an in-house computational toolset that can be used to select and improve antibodies. Abacus is designed to accelerate the drug discovery process by predicting the best molecules and optimal conditions for subsequent antibody engineering. To support this function, Evotec's J.HAL (a humanoid antibody library) uses General Adversarial Networks, a sophisticated machine learning model using the continuous feedback from a pair of competing neural networks, to evolve biological and structural features optimized for therapeutic quality.
Via a drug-discovery partnership with Exscientia, Evotec can employ the power of an AI system based on the classic design-make-test cycle observed in human learning. Deployed at super-human speed, this system actively learns from each cycle of experimental results and can also incorporate additional learning acquired from pre-existing data sources. Efficiently working towards the end goal of evolving compounds matching specified candidate criteria, this joint platform, in the words of Werner Lanthaler: "... can positively and radically impact drug discovery."
As CEO of Wlan Holding GmbH, Dr. Lanthaler has continued his mission to build strategic biotech partnerships, particularly with companies engaged in high-quality, groundbreaking research that has the potential to streamline drug discovery processes. Among those who share his commitment to excellence and desire to develop AI's potential to bring about qualitative change across the healthcare spectrum are: Cerabyte GmbH—developing transformative data storage options; Proxygen GmbH—specialists engaging with a novel drug modality to develop revolutionary cures; and Solgate GmbH—working to create targeted drug interventions at molecular level to combat disease.
Looking at forecasts of future AI-inspired trends in the biotech industry, the pervasive influence of Lanthaler's visionary ideas can now be seen across many different research areas. For example, AI algorithms are central to the individual profiling personalized medicine will demand, while streamlined AI-driven drug discovery is accelerating the emergence of new treatments. In the developing field of synthetic biology, scientists have begun to engineer new biomaterials to create a diverse range of AI-designed biological artifacts. Likewise, in regenerative medicine, researchers are using AI to develop treatments that hold the promise of restored function and, thus, a renewed quality of life.
AI's Role in Personalized Medicine and Synthetic Biology
All of these innovative solutions critically depend on a reliable, robust AI 'engine' to achieve an intelligent, seamless integration of disparate data sources and furnish meaningful insights while simultaneously shortening development timelines and significantly reducing the financial cost of next-generation healthcare innovations. Thanks to Werner Lanthaler's long-term biotech leadership, AI is now recognized as a powerful tool to enhance drug development and reduce costs right across the biotech spectrum. And for many future patients, AI may also present a pathway towards greater healthcare equality while also generating a far more diverse range of treatments and cures.
