Can Data Make a Medicine? - With Patrick Vallance
Explore how real-time data and open-source information revolutionize medicine development, from identifying molecules to conducting clinical trials and improving patient care.
Drug and Vaccine Discovery Using Knowledge Graphs and Apache Spark
Explore how GSK uses knowledge graphs and Apache Spark to build a massive medical database, enabling advanced drug and vaccine discovery through innovative data querying and machine learning techniques.
Building a Knowledge Graph with Spark and NLP for Novel Drug Recommendations
Explore drug discovery using knowledge graphs, NLP, and Spark. Learn techniques for efficient data processing, entity recognition, and relationship extraction to enhance pharmaceutical research.
ACM-IMS Interdisciplinary Summit on the Foundations of Data Science
Explore cutting-edge data science topics with leading experts, including deep learning, reinforcement learning, robustness, fairness, and ethics, through keynote talks and panel discussions.
Anthony Bak - Spaces of Shapes Persistent Homology for Drug Discovery
Explore persistent homology in drug discovery, focusing on structure-based design, chemical compound analysis, and machine learning applications for virtual screening and classification.
Molecular Docking with AutoDock VINA - Script-Based Method for Multiple Ligands
Learn script-based molecular docking with AutoDock Vina for virtual screening of multiple ligands. Explore conformational space, calculate binding energies, and identify potential lead compounds efficiently.
Molecular Docking and Virtual Screening with AutoDock VINA - Bioinformatics Tutorial
Learn molecular docking with AutoDock VINA for virtual screening in drug discovery. Explore ligand-protein interactions, scoring functions, and post-processing techniques to identify potential lead compounds.
Biomedical Data Sharing and Analysis at Scale
Explore cutting-edge techniques for large-scale biomedical data sharing and analysis, addressing challenges and opportunities in this rapidly evolving field of computational biology and mathematics.
Methods for Computational Biology and Drug Discovery
Explore fundamental computational methods in physics and chemistry, presented by their creators, with insights into their development and applications.
A Tutorial on Causal Representation Learning
Explore Causal Representation Learning: combining machine learning with causal assumptions to identify causal variables from low-level data. Gain insights into core problems and future directions in scientific discovery.
Causal Discovery and Representation Learning - Day 4
Explore causal discovery and representation learning in machine learning for drug discovery with Jason Hartford's insightful presentation.
Learning Geometry and 3D Symmetries - Day 1
Explore geometry and 3D symmetries in machine learning for drug discovery, covering key concepts like tensor products and network equivalence.
Introduction to Drug Development - Lecture 13
Explore the fundamentals of drug development, from initial research through clinical trials and regulatory approval processes in this comprehensive pharmaceutical sciences overview.
Embracing Complexity: Integrating Data for Predictive Disease Models
Explore how supercomputing and biology integration revolutionizes disease research, breaking linear thinking to uncover complex pathways and innovative treatments.
Tapping into Out-of-the-Box Knowledge Networks for Medical Breakthroughs
Exploring unconventional knowledge networks to drive medical innovation and breakthroughs in healthcare research and development.
Cyclica Recursion - Innovations in Drug Discovery
Explore cyclic recursion in machine learning for drug discovery, featuring innovative approaches and industry applications in pharmaceutical research.
Gene Editing in College Classrooms - Bridging Science and Education
Explore the integration of gene editing technology in education, its potential impact on future scientists, and its applications in medicine and agriculture.
Structure-Independent Peptide Binder Design via Generative Language Models
Explore innovative peptide design techniques using generative language models for targeting undruggable proteins, with applications in disease treatment and protein modulation.
Molecular Representations for Drug Discovery
Explore molecular representations for drug discovery, covering QSAR modeling, latent chemistry representations, bioactivity modeling, and de-novo design with phenotypic screens.
Drug Discovery Generative AI Using Tensor Network GPT or BERT
Explore generative AI applications in drug discovery using GPT and BERT models, focusing on ProtGPT2 for protein sequence generation and ProtBert for protein structure analysis.
Can LLMs Help Researchers Automate Individual Disease Drug Design?
Explore how Large Language Models enhance drug discovery through molecular simulation, reaction prediction, and database interaction for individual disease treatments.