Meta QSAR
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Presentation on using the Discovery Bus to develop a new field of research, "Meta QSAR" the comparative study of QSAR modelling methodology. Given at UK QSAR Society meeting at Syngenta October 22nd 2009
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AI is the Future of Drug Discovery
Artificial intelligence (AI) has the potential to transform drug discovery through the use of semantic networks to represent biomedical knowledge as facts and infer new insights, probabilistic rules to evaluate beliefs about candidate drugs, and optimization and simulation techniques to iteratively improve outcomes. By continuously learning from diverse sources of data, AI systems could automate and enhance the processes of target identification, compound screening and testing in ways that accelerate research and development compared to traditional analytical tools.
Most Drug Discovery Scientists could be replaced by Software Systems
The document discusses how drug discovery scientists could potentially be replaced by software systems in the future. It argues that drug discovery has become a mature field with established methodologies and best practices. It is presented as a multi-objective optimization problem that considers many potential drug targets, compounds, and goals. The document proposes that human understanding is no longer essential in drug discovery and that systems could select which compound to synthesize next through computational models and rules. It then provides examples of how expert strategies, workflows, and a "panel of experts" approach could be modeled computationally through packages, rules, and optimization engines to enable more automated "declarative drug design".
InkSpot Science presentation at Open Science Meeting
InkSpot Science is an on-demand computing platform that provides scientists with hosting, content management, data visualization, and collaboration tools as an alternative to desktop applications. It offers secure hosting of various data types including tables, graphs, equations, blogs and wikis. Users can design workflows, access specialized services for tasks like drug discovery, and take advantage of social networking features to share information.
PBPK simulation as an alternative to animal testing
Generic physiologically based pharmacokinetic (PBPK) models for rats and humans were developed using in vitro absorption, distribution, metabolism, and excretion (ADME) data. The models accurately predicted plasma concentrations of drugs in independent test sets, performing equally to or better than traditional allometric scaling methods. However, PBPK models have advantages over scaling in accounting for differences in drug chemistry, metabolism, and administration routes between species.
Forager Poster
Forager is a multi-objective reverse QSAR search agent that was developed by researchers from Newcastle University and the Research Centre for Cheminformatics in Belgrade. The agent uses multiple objectives to search chemical structure databases for compounds with desired properties based on quantitative structure-activity relationship models. Forager allows for the balancing of multiple desired properties during the reverse search.
Colonist
Genetic algorithms use genetic operators like mutation and crossover to evolve molecules. The researchers developed new programming concepts to apply genetic operators to reduced molecule representations to enable powerful molecular evolution. They introduced genetic operators that produce variation in reduced representations as a fundamental process for molecular evolution.
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Meta QSAR
- 1. Meta QSAR David Leahy
- 2. Structures Data Descriptor Descriptor Descriptor Descriptor Test Set Filter Filter Filter Filter Model Model Model Model Transform Transform Train Train Predict Predict Predict Predict Predict Predict Predict Predict
- 3. Calculate Descriptors Filter feature request ... Model build request ... Calculate descriptors request ... responses responses responses Planner Model Build Filter Features
- 4. Calculate Descriptors Filter feature request ... Model build request ... Calculate descriptors request ... responses responses responses responses responses responses Planner Model Build Filter Features Calculate Descriptors
- 19. Meta QSAR: Automation of Decision Making in QSAR Modelling
- 21. EBI Stardrop Database (~2-3000 datasets)
- 24. Amazon Master Server Amazon Worker Servers Azure Worker Servers Security Virtual Teams Inkspot Science Sci Software Services Workflow Cloud
- 26. Inkspot Install to Azure
- 28. Domain of applicability and local models
- 30. New methods install and benchmark
- 31. New data series