The application of Artificial Intelligence/Machine Learning (AI/ML) methods in drug discovery are maturing and their utility and impact is likely to permeate many aspects of drug discovery. Numerous methods, however, utilize structure-activity relationship (SAR) data without explicit use of 3D structural information of the ligand protein complex. Gilead is using BIOVIA’s Generative Therapeutics Design solution (GTD) to take advantage of 3D structural models, i.e. pharmacophoric representation of ligand protein interaction as well as typical docking/scoring steps. Using Gilead’s SAR data set pertaining to the discovery of spleen tyrosine kinase (SYK) inhibitors Entospletinib and Lanraplenib they found that common types of problems in medicinal chemistry can be effectively addressed via GTD.
Resource Tag: Harnessing AI To Expedite R&D
The JUMP-Cell Painting Consortium
JUMP-Cell Painting Consortium created a new data-driven approach to drug discovery based on cellular imaging, image analysis, and high dimensional data analytics
Trustworthy AI
Our speakers, Navdeep Gill (H2O.ai) and Chas Nelson (gliff.ai) present a perspective on trustworthy and responsible AI.
They discuss various components that contribute to responsible AI and the new ANSI standard “ANSI/CTA 2090 Use of Artificial Intelligence in Health Care: Trustworthiness” and the ways to implement trustworthy and responsible AI in practice covering the whole AI lifecycle.
Valuation of Artificial Intelligence Technology Investments
Today, the hype that surrounded artificial intelligence in the previous years is largely gone, and the industry practitioners are looking for solid use cases and proof of value. Prashant Natarajan, VP of H2O.ai, and Dr. Peter Henstock, ML&AI Technical Lead Technical Lead, Pfizer, will discuss the methods for valuation of artificial intelligence investments in the pharmaceutical industry.
AI Tools for Drug Design: Autodesigner, A De-novo Design Algorithm
The lead optimization stage of a drug discovery program generally involves the design, synthesis, and assaying of hundreds to thousands of compounds. The design phase is usually carried out via traditional medicinal chemistry approaches and/or structure-based drug design (SBDD) when suitable structural information is available. Two of the major limitations of this approach are (1) difficulty in rapidly designing potent molecules that adhere to myriad project criteria, or the multiparameter optimization (MPO) problem, and (2) the relatively small number of molecules explored compared to the vast size of chemical space. To address these limitations we have developed AutoDesigner, a de novo design algorithm.
Speaker
- Karl Leswing, Machine Learning Tech Lead, Schrödinger
Combining Robotics and Machine Learning for Accelerated Drug Discovery
Artificial intelligence has an increasing impact on drug discovery and development, offering opportunities to identify novel targets, hit, and lead-like compounds in accelerated timeframes. However, the success of any AI/ ML model depends on the quality of the input data, and the speed with which in silico predictions can be validated in vitro. The talk will cover laboratory automation and robotics and the benefits they offer in terms of quality and speed of data generation synergized with AI/ ML-powered drug discovery approaches.
This webinar will cover some of the general trends in the industry, and also highlight successfully implemented case studies that show how the combination of robotics and AI/ ML lead to accelerated project timelines and superior research outputs.
Speaker
Martin-Immanuel Bittner, MD, DPhil, FRSA is the Chief Executive Officer of Arctoris, the drug discovery platform company that he co-founded in Oxford. Martin graduated as a medical doctor from the University of Freiburg in Germany, followed by his DPhil in Oncology as a Rhodes scholar at the University of Oxford. He has extensive research experience covering both clinical trials and preclinical drug discovery and is an active member of several leading cancer research organizations, including EACR, AACR, and ESTRO. In recognition of his research achievements, he has been elected as a member of the Young Academy of the German National Academy of Sciences and of Sigma Xi.
Artificial Intelligence in Drug Discovery: What Is Realistic, What Are Illusions?
Although artificial intelligence (AI) has had a profound impact on areas such as image recognition, comparable advances in drug discovery are rare. We will discuss the stages of drug discovery in which improvements in the time taken, success rate or affordability will have the most profound overall impact on bringing new drugs to market. Changes in clinical success rates will have the most profound impact on improving success in drug discovery; in other words, the quality of decisions regarding which compound to take forward (and how to conduct clinical trials) are more important than speed or cost.
Although current advances in AI focus on how to make a given compound, the question of which compound to make, using clinical efficacy and safety-related end points, has received significantly less attention. As a consequence, current proxy measures and available data cannot fully utilize the potential of AI in drug discovery, in particular when it comes to drug efficacy and safety in vivo. Thus, addressing the questions of which data to generate and which end points to model will be key to improving clinically relevant decision-making in the future.
Speaker
Andreas Bender, PhD QJ
Reader for Molecular Informatics, Centre for Molecular Informatics
Department of Chemistry, University of Cambridge
Director Digital Life Sciences
Innovation Campus Berlin (ICB)/Nuvisan
Challenges in the Regulation of AI Software as a Medical Device
Software as a medical device (SaMD) that leverages artificial intelligence (AI) has the opportunity to reshape healthcare. It also raises unique challenges for developers and regulators. As healthcare advances and digital solutions leveraging AI become more prevalent, it is important that medical device regulatory frameworks also advance to match the speed of innovation.
This panel will review key terms related to AI SaMD and describe unique regulatory challenges associated with devices that leverage AI. Additionally, the panel will explore novel regulatory approaches to the regulation of AI SaMD currently under consideration by international regulatory authorities.
Speakers
- Nathan A. Carrington, Ph.D. Head of Digital Health and Innovation, Roche
- Pat Baird Sr. Regulatory Specialist – Head of Global Software Standards, Philips
- Loganathan Kumarasamy, Head of Scientific Informatics, Validation and Compliance services, North America, Zifo R&D
Building the Future of Collaborative Research with Federated Learning
Federated learning is a new machine-learning paradigm where multiple partners can collaborate on complex research questions without centralizing or sharing data outside of their organizations.
This ‘collaborative machine learning’ approach enables data science teams to work on larger and more diverse datasets, previously inaccessible, boosting the predictive power of machine learning algorithms and enhancing AI capabilities. By overcoming privacy and confidentiality concerns, companies can build partnerships and consortia and retain their competitive edge.
For example, the MELLODDY consortium pioneers federated learning-based drug discovery across 10 pharma companies benefiting from the collective insights of the world’s largest cheminformatics data network where each participant retains full confidentiality and governance over their molecular libraries.
Federated learning in healthcare can also facilitate knowledge transfer between medical researchers and data scientists, bridging the gap between AI and clinical care. The HealthChain project is a successful demonstration that an algorithm can be trained on siloed histology images, distributed across different hospitals, to predict treatment responses in breast cancer. Together with clinical, research, and technology partners, we demonstrated improved robustness and performance of the technology over locally trained algorithms.
With the platform deployed and used reliably in a production environment, the stage is set for further collaborative research projects and eventually clinical applications in cancer, heart failure, and other therapeutic areas.
Speakers
- Victor Dillard, Commercial Operations Director, Owkin
- Hugo Ceulemans, Scientific Director Discovery Data Sciences, Janssen
- Dr. Guillaume Bataillon, Pathologist, IUCT Oncopole
Optimizing Kinase Profiling Programs with Deep Learning
Join Genentech and Optibrium for this discussion of Alchemite™, a novel deep learning approach, and its application to optimizing kinase profiling programs. Using Alchemite™ reduces the number of kinase assays required to accurately predict the full kinase selectivity profile, effectively accelerating experimental programs.
The team will demonstrate the method’s performance on a data set of approximately 650 kinases and 10,000 compounds, significantly outperforming state-of-the-art quantitative structure-activity relationship (QSAR) approaches, including multi-target deep learning. Furthermore, we will discuss Alchemite’s unique ability to provide reliable prediction-uncertainty-estimates that enable the selection of the most informative kinase assays and which compounds to test.
Ideal for:
Scientist, Sr. Scientist, Program Manager, Associate Director, Director
Speakers
- Matt Segall, CEO, Optibrium
- Samar Mahmoud, Senior Scientist, Optibrium
- Fabio Broccatelli, Senior Scientist, Genentech
Best Practices for Artificial Intelligence in Life Sciences Research
Recently, artificial intelligence (AI) and machine learning (ML) technologies in the biotechnology and pharmaceutical industries moved beyond experiments conducted by a few dedicated specialists and entered the industry mainstream. With this transition comes the need to formalize and publish the lessons learned by early adopters. These lessons are not limited to the technology of AI, but rather are the best ways to apply AI methods in a business environment. In medicine, such lessons are already being recorded; however, these recommendations are extremely specific to medical practice and not broadly applicable to drug discovery and biotech. This paper is an attempt to close that gap.
Technical Strategies Against Bias in AI
There is an increasing number of reports discussing the urgent need for addressing bias in decision-making algorithms in healthcare. In fact, a recent JAMA commentary published in 2021 highlighted systemic kidney transplantation inequities for black individuals. With AI-based and machine learning techniques increasingly playing a role in healthcare decision-making, it becomes necessary to discuss not only the ethical implications but solutions and approaches to detect and reduce the impact of computer bias in healthcare.
In this webinar on-demand, industry experts will share lessons learned and discuss possible solutions.
Ideal for:
Manager, Sr. Manager, Director, CSO, CIO, CEO
Speakers
- Peter Henstock, PhD, Machine Learning & AI Technical Lead, Pfizer
- Helena Deus, PhD, Biomedical Semantic Solutions Lead, ZS Associates
- Margi Sheth, R&D Data Policy Director, AstraZeneca
- Prashant Natarajan, Vice President of Strategy & Products, H20.ai