NIH VideoCast - In silico Drug Discovery Workshop-Day 1

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In silico Drug Discovery Workshop-Day 1

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Air date:	Wednesday, October 23, 2024, 11:00:00 AM Time displayed is Eastern Time, Washington DC Local
Views:	Total views: 1,950 (1,191 Live, 759 On-demand)
Category:	Special
Runtime:	05:35:19
Closed captioning:	Check box to display CC outside of the video Note: You can drag the captioning window around and resize it
Description:	The National Center for Advancing Translational Sciences (NCATS) Assay Guidance Manual (AGM) program is hosting a two-day workshop that will cover a broad range of critical concepts, including practical approaches and best practices, to successfully develop and integrate accurate, robust, and rigorous AI/ML methods/models into drug discovery pipelines and to facilitate discussions around utilization of such methodologies. Many of the speakers are pioneers of covered scientific topics and the listed workshop sessions aim to share the fundamentals and modern knowledge to facilitate learning for both novice and established practitioners. This workshop is jointly organized by NCATS, Biomedical Advanced Research and Development Authority (BARDA), University of California San Diego (UCSD), and The University of North Carolina at Chapel Hill (UNC). This workshop aims to provide scientists with best practices and standards for rigor in the field of computational drug discovery to enable accurate and reproducible results. This workshop will also cover case studies for AI-driven drug discovery campaigns as well as an overview of new trends and gaps in the field. Specific learning goals and objectives of this workshop include: * Provide participants with data sources and best practices in building and maintaining databases used for developing robust and rigorous AI based drug discovery models/methods. * Introduce participants to the available computational methodologies utilized in drug discovery and discuss their utility and limitations. * Provide case studies for digital drug discovery and an overview of new trends in the field. * Provide guidelines and considerations for developing robust and reproducible in silico models. * Discuss challenges in data quality and data sharing as well as affordability, accessibility, transferability, accuracy, and reproducibility of AI-driven computational techniques. * Identify gaps in translation of these in silico models to therapies and seed discussions around best practices to help bridge the gaps in the field.

Author:	NCATS

CIT Live ID:	55158
Permanent link:	https://videocast.nih.gov/watch=55158

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