SaaS Metrics
Abstract
Evaluating pharmacokinetic properties of small molecules is considered a key feature in most drug development and high-throughput screening processes. Generally, pharmacokinetics, which represent the fate of drugs in the human body, are described from four perspectives: absorption, distribution, metabolism and excretion—all of which are closely related to a fifth perspective, toxicity (ADMET). Since obtaining ADMET data from in vitro, in vivo or pre-clinical stages is time consuming and expensive, many efforts have been made to predict ADMET properties via computational approaches. However, the majority of available methods are limited in their ability to provide pharmacokinetics and toxicity for diverse targets, ensure good overall accuracy, and offer ease of use, interpretability and extensibility for further optimizations. Here, we introduce Deep-PK, a deep learning-based pharmacokinetic and toxicity prediction, analysis and optimization platform. We applied graph neural networks and graph-based signatures as a graph-level feature to yield the best predictive performance across 73 endpoints, including 64 ADMET and 9 general properties. With these powerful models, Deep-PK supports molecular optimization and interpretation, aiding users in optimizing and understanding pharmacokinetics and toxicity for given input molecules. The Deep-PK is freely available at https://biosig.lab.uq.edu.au/deeppk/.
Academic Publication Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction
Research Abstract & Technology Focus
Correlated Market Trend: Adaptive Learning
Bridging academia to market: The 60-day public search velocity mapping directly to the core technology of this paper. Dashed line represents 7-day moving average.
AI Semantic Synergy Context
Connecting this academic literature to real-world market discussions and products.
Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction
Abstract Evaluating pharmacokinetic properties of small molecules is considered a key feature in most drug development and high-throughput screening processes. Generally, pharmacokin...
ProTox 3.0: a webserver for the prediction of toxicity of chemicals
Abstract Interaction with chemicals, present in drugs, food, environments, and consumer goods, is an integral part of our everyday life. However, depending on the amount and duration...
Testing the predictive power of reverse screening to infer drug targets, with the help of machine learning
AbstractEstimating protein targets of compounds based on the similarity principle—similar molecules are likely to show comparable bioactivity—is a long-standing strategy in drug research. Having pr...
ADMET-AI: a machine learning ADMET platform for evaluation of large-scale chemical libraries
Abstract Motivation The emergence of large chemical repositories and combinatorial chemical spaces, coupled with high-throughput docking a...
Have protein-ligand cofolding methods moved beyond memorisation?
Abstract Deep learning has driven major breakthroughs in protein structure prediction, however the next critical advance is accurately predicting how proteins int...
Frequently Asked Questions (FAQ)
Curated market intelligence mapped to this research.
What is the core focus of the research titled 'Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction'?
This literature focuses on: Abstract Evaluating pharmacokinetic properties of small molecules is considered a key feature in most drug development and high-throughput screening processes. Generally, pharmacokinetics, which represent the fate of drugs in the hu...
Are there open-source GitHub repositories related to Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction?
Yes, open-source projects like THU-MAIC/OpenMAIC (Open Multi-Agent Interactive Classroom — Get an immersive, multi-agent learning experience in just one click) are actively building upon these concepts.
Which startups are commercializing the technology behind Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction?
Products like GitHub Stacked PRs are bringing this to market. Their focus is: Break big changes into small reviewable PRs.
What other academic literature is closely related to 'Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction'?
Yes, highly correlated activity was mapped. An entry titled 'Deep-PK: deep learning for small molecule pharmacokinetic and toxicity prediction' discusses this: Abstract Evaluating pharmacokinetic properties of small molecules is considered a key feature in most drug development and high-thro...
Cite this Market Intelligence Report
Reference our AI-mapped synergy between this research and the commercial market to instantly build authority.
Commercial Realization
Startups and Open Source tools heavily associated with the concepts explored in this paper.
-
GitHubTHU-MAIC/OpenMAIC
-
GitHubdanveloper/flash-moe
-
Product HuntGitHub Stacked PRs
-
Product HuntPadel Chess