BIOT Spotlight:
AI & Machine Learning
Four different sessions are available for abstract submissions.
Elucidating drug/pathway targets
The rapid advancement of artificial intelligence (AI) and machine learning (ML) technologies is revolutionizing the field of drug discovery and development. While there is a lot of focus on discovery of novel chemical entities and biologics using these AI/ML technologies, less effort and attention has been dedicated to elucidating targets implicated in disease biology. By integrating vast datasets from genomics, proteomics, and pharmacology, AI/ML models are enabling researchers to uncover novel therapeutic targets and understand complex biological mechanisms with unprecedented accuracy at multiple scales. In this session, we will delve into cutting-edge applications of AI/ML technologies in identifying and elucidating targets and biological pathways important for drug discovery and development. We invite experts in the field to submit abstracts on their groundbreaking research and case studies showcasing how these technologies are being used to predict drug-target interactions, identify targets and pathways in a disease-specific and disease-independent manner (i.e., what interactions make a compound into a drug), and for lead optimization. We are looking for topics which focus not only on targets and pathways involved in efficacy but also those necessary for absorption, dispersion, metabolism, and excretion (ADME) and minimal toxicity. Abstracts that reveal insights into the latest algorithms and computational models that drive these mechanistic discoveries, including deep and reinforcement learning, natural language processing, and generative and evolutionary multiscale models are welcome. Furthermore, we are interested in submissions that address the challenges and limitations of applying AI/ML in drug discovery, such as data quality, model interpretability, and regulatory considerations. Abstracts that describe strategies for overcoming these hurdles and translating AI-driven discoveries from the virtual and wet benches to the bedside are also welcome.
Designing protein-based drugs and other products
In the past decade, artificial intelligence has had huge impact on protein design, ranging from AI-guided protein folding and design to large-scale dataset curation and digitization. In this session, we encourage submissions related to novel applications of AI to protein design including novel algorithms and representations, methods for designing products for activity (e.g. binding, enzymatic activity, or other functions), methods for designing products for increased manufacturability or stability, methods for designing products for synthetic biology applications, and methods for design different types of biological products (including proteins and other modalities). We additionally, encourage submissions related to integrating experimental and machine learning strategies such as innovations that couple high throughput screening with machine learning or active learning-guided experimental design. Finally, we encourage submissions that explore challenges with curating the datasets required for machine learning such as approaches for curating historical data, combining data from multiple sources, or federated learning techniques.
Improving speed and performance in process development
This session aims to explore the transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) in advancing speed and performance within bioprocess development. To showcase groundbreaking research and insights in this rapidly evolving field, we invite abstracts on a wide range of topics to enrich the dialogue, including, but not limited to:
- Autonomous Process Optimization: Abstracts presenting innovative approaches and case studies that illustrate the implementation of AI and ML techniques in autonomous process optimization for bioprocess development. We welcome submissions that highlight real-time adjustment capabilities, enhanced process efficiency, improved control strategies, and optimization of key bioprocess parameters.
- Hybrid Modeling Algorithms: Abstracts showcasing the development and application of hybrid modeling algorithms for bioprocess development. We encourage submissions that discuss the integration of multiple AI and ML methods, such as neural networks, genetic algorithms, support vector machines, surrogate modelling, and more, to enhance process understanding, prediction accuracy, and optimization of complex bioprocesses.
- Integration with High Throughput Automation and Experimentation: Abstracts focusing on the integration of AI and ML systems with high throughput automation and experimentation platforms in the context of bioprocess development. We invite submissions that explore how these technologies accelerate data acquisition, enable rapid analysis, and support informed decision-making, leading to more efficient and effective bioprocess development.
- Predictive Modeling for Bioprocess Scale-up: Abstracts addressing the application of AI and ML in predictive modeling for bioprocess scale-up. We encourage submissions that discuss the use of modeling techniques to guide process transferability, scalability, and optimization across different scales, ensuring robust and reliable manufacturing processes.
- In-Silico Manufacturing Assessment: in-silico methods to screen large numbers of molecule candidates for platform fit and/or manufacturability, ultimately resulting in speed to clinic and easing the burden of process development.
We welcome researchers and practitioners to submit abstracts that present original research, novel methodologies, successful case studies, and practical applications in bioprocess development. We eagerly anticipate your contributions to this compelling session and look forward to engaging discussions on the application of AI and ML in improving speed and performance in bioprocess development.
Successes and challenges in deploying AI/Machine Learning in biomanufacturing
Artificial Intelligence (AI) and Machine Learning (ML) have emerged as pivotal tools in the biomanufacturing sector, revolutionizing how we design, optimize, and control processes critical to producing biopharmaceuticals, biomaterials, and other biologically derived products. This session will explore the transformative role of AI and ML in enhancing efficiency, quality, and innovation in end-to-end biomanufacturing processes. We will delve into both the successes and challenges encountered when deploying AI and ML technologies in biomanufacturing. Through case studies and discussions, we will examine the latest advancements, innovative applications, and the future potential of AI/ML in this rapidly evolving field.
We invite scientists, researchers, and industry leaders from academia and the biopharmaceutical sector to join us in exploring the transformative potential of AI and ML in biomanufacturing by submitting their abstracts targeting one of the following topics:
- Data management and cybersecurity in AI/ML applications
- Cutting-edge progress in AI/ML applications for:
- Bioprocess optimization and continued process verification
- Bioprocess monitoring and control
- Predictive modeling and simulation
- Quality control and assurance
- Facility operational optimization (i.e. scheduling, equipment performance and maintenance monitoring, inventory management, etc.)
- Regulatory considerations and challenges in adopting AI/ML based technologies