| "Cheminformatics" | |||
| ▼IL01-01 | Gregory Landrum（Eidgenössische Technische Hochschule Zürich） | 10/28 14:10-14:50 | |
| "A personal perspective on open science, open source, and open data" | |||
| ▼IL01-02 | Yoshihiro Yamanishi (Nagoya University) | 10/28 14:50-15:30 | |
| "Data-driven identification of therapeutic targets and drug candidates through machine learning" | |||
| "Advancing Drug Discovery Through Medical Data: From Analytics to Target Identification" | |||
| ▼IL02-01 | Tomohiro Kuroda (Kyoto University Hospital) | 10/29 14:00-14:30 | |
| "The Amended Next Generation Infrastructure Act enhances the data-driven drug discovery" | |||
| ▼IL02-02 | Ryo Kunimoto (Daiichi Sankyo Co., Ltd.) | 10/29 14:30-15:00 | |
| "Utilization of Omics Data for Exploration of Drug Target Candidates" | |||
| ▼IL02-03 | Tohru Ishitani (Osaka University) | 10/29 15:00-15:30 | |
| "-Synthetic biology of healthy longevity-Synthesizing longevity in the extremely short-lived killifish" | |||
| "Generative AI: The Knowledge Engine Weaving the Future" | |||
| ▼IL03-01 | Jun Suzuki (Center for Language AI Research, Tohoku University） | 10/30 14:00-14:30 | |
| "Predicting the Evolution of Generative AI: Trends Today and What Lies Ahead" | |||
| ▼IL03-02 | Kazue Mizuno (Preferred Networks, Inc.) | 10/30 14:30-15:00 | |
| "Initiatives in Leveraging Generative AI in the Life Sciences Field at Preferred Networks" | |||
| ▼IL03-03 | Kosuke Nakago (Sakana AI) | 10/30 15:00-15:30 | |
| "Sakana AI's directions in generative AI research" | |||
I have been writing open-source scientific software and advocating for a more open practice of science in chemistry for almost 20 years. In that time a large shift towards increased openness has taken place in the norms and common practices in our field. In this talk I provide an argument for why openness in science is important, try to debunk some common myths about open science, and look back at how things have changed for the better. I will argue that by staying pragmatic and focused on the most important aspects we can continue to improve and will end up doing better science faster.
Recent developments in biotechnology have contributed to the increase in the amounts of high-throughput data in the genome, transcriptome, proteome, interactome, phenome and diseasome. At the same time, the high-throughput screening of large-scale chemical compound libraries with various biological assays is enabling us to explore the chemical space of possible compounds. These big data can be useful resources for drug development processes. Artificial intelligence and machine learning methods are expected to play key roles in the big data analysis. In this study, we developed novel machine learning methods for various applications in drug discovery. First, we present a computational approach for therapeutic target identification. A critical element of drug development is the identification of therapeutic targets for diseases, but the depletion of therapeutic targets is a serious problem. Here we propose to integrate genome-wide and transcriptome-wide association studies for predicting new therapeutic targets of various diseases including orphan diseases. Next, we present a network-based and structure-based approach for discovering therapeutic and side effects of drug candidate compounds. The prediction is performed based on molecular interaction networks and genome-side scale protein structures revealed by AlphaFold. Finally, we present a computational approach for de novo drug design. Most previous methods are based on chemical information, but they do not take into account biological information. Here we propose to utilize various biological data and polypharmacology toward the de novo design of drug candidate molecular structures with desired properties.
The Next-Generation Medical Infrastructure Act, which came into force in May 2018, was enacted as a special law of the Personal Information Protection Act to promote drug discovery science and medical device development. However, due to many restrictions, such as the fact that the information that can be used is limited to anonymously processed medical information, it has not been widely used. Therefore, the amended law, which came into force in April 2024, introduced new concepts such as pseudonymized medical information and linkable anonymously processed medical information, making it possible to submit data for pharmaceutical purposes and link with public databases such as NDB, expanding the possibilities for using medical information. In addition, nowadays, the provision of pseudonymized information from public databases such as NDB has been considered, and it is expected that more advanced data utilization will become possible in the near future. In this lecture, I will provide an overview of the revised Next-Generation Medical Infrastructure Act and of how to promote drug discovery research by making the most of it. In addition, I will discuss the form of the medical information infrastructure system to further accelerate medical data science, taking the idea of the European Health Data Space (EHDS), which is a pioneer in the development of medical data infrastructure, as a mirror.
To extend healthy lifespan, it is necessary to establish technology to control whole-body aging. In recent years, molecular cell biology research has led to a rapid understanding of aging mechanisms at the cellular level, and bioinformatics analysis has led to the identification of longevity factors and disease risk factors. However, there has been little progress in understanding the control mechanisms of aging at the whole-body level, and in developing approaches to control it. In this symposium, I will talk about our new approach, "the synthetic biology of healthy longevity". Specifically, we introduce candidates for healthy longevity factors found through comparative analysis between human individuals, animal species, animal strains, and males and females with different lifespans and healthspans into the ultra-fast aging killifish (Nothobranchius furzeri), and artificially create healthy longevity to rapidly search for factors necessary for achieving healthy longevity. By taking this approach, we hope to accelerate the discovery of new technologies to extend healthy lifespan and develop them into the future realization of healthy longevity in humans.
In generative AI for text, as exemplified by ChatGPT, language models based on deep neural networks (Transformers) play a central role. In the approximately three years since ChatGPT’s launch at the end of November 2022, researchers have conducted both theoretical (mathematical) and empirical studies to probe its underlying capabilities from a wide range of perspectives.
For example, findings to date suggest that the fundamental rationale underlying the performance of language models lies in the fact that scaling laws hold with respect to both the amount of training data and the number of model parameters. Similarly, researchers have now discovered that a scaling law for test-time computation holds when an appropriate method is employed. Moreover, with the same amount of training data, higher-quality data yields superior performance. As another example, some studies in the field of mechanistic interpretability have examined how models encode knowledge and are now focusing on developing steering methods for interventions that require no further training. Furthermore, numerous studies have pointed out that language models, on their own, struggle to entirely prevent the generation of false information (so-called "hallucinations") and to fully guarantee safety without relying on external modules.
In this talk, I will cover a few major topics regarding the strengths and weaknesses of recent language models and draw on their underlying rationale to discuss my analyses and interpretations of current progress and future developments.
In this talk, I will revisit the strengths, weaknesses, and functional enhancements of the language models discussed above, and drawing upon relevant theoretical justifications, empirical observations, and analytical findings, I aim to offer my own perspective on the current progress and future developments of language models.
Preferred Networks (PFN) is developing AI-based businesses in the life sciences and healthcare fields, including drug discovery. Products and services in these fields are used by a wide range of people, including researchers, medical professionals, patients, and the general public, and gaining the trust of users is essential. PFN has long established its own AI policy as an ethical guideline. With the introduction of generative AI-related technologies, the company has been working to develop more practical processes and systems. Specifically, PFN has established a governance promotion organization to manage risks associated with AI products and services, and is building and operating internal review processes and an AI governance framework.
In this presentation, we will first introduce PFN's services and research examples in the life sciences and healthcare fields. Next, we will outline how we have considered AI governance in the development and operation stages for some of these examples.
Through this presentation, we hope to share the challenges of implementing AI technologies in society and practical approaches to solving them.| Invited Talk "Generative AI: The Knowledge Engine Weaving the Future" IL03-03 |
[Big hall] 10/30 15:00-15:30 | |
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Sakana AI's directions in generative AI research | |
| Kosuke Nakago | ||
| Sakana AI | ||
We will introduce several key initiatives from Sakana AI's research and development in generative AI, with a focus on approaches in scientific field.
First, we will outline an approach we have been exploring since company’s foundation: forming a collective intelligence through the combination of diverse, existing foundation models. Specific examples of this approach include Evolutionary Model Merge (EMM) and Adaptive Branching Monte Carlo Tree Search (AB-MCTS).
Establishing appropriate evaluation metrics is essential for unleashing the applicability of generative AI to next level. We will present our efforts to promote research in these areas by proposing new benchmarks and their agents, including ALE-Bench, AI CUDA Engineer, Sudoku-Bench and EDINET-Bench.
Furthermore, we will discuss our endeavors to automate the entire research process. We will feature AI Scientist, which handles tasks from ideation and experimentation to paper writing. Its successor, AI Scientist v2, generated a paper that passed the peer-review process for an ICLR 2025 Workshop.
While AI agent workflows are conventionally designed by humans, we are also exploring an approach that delegates this construction process to an AI, or a "Meta Agent." Examples of this line of research include the Automated Design of Agentic Systems (ADAS) and the Darwin Gödel Machine (DGM).