Speaker
Description
For healthcare and drug discovery, particularly during the earlier stages of target identification and hit finding, recent advancements in data availability, computational power, and methods (machine learning (ML) and artificial intelligence (AI)) have shown great promise to further rationalized the drug development pipeline initiating a transition from a “data generation and triaging” to a ”result prediction and verification” pipeline.
We present different feature-based concepts of predicting in vivo PK profiles for chemical and biological identities derived from their chemical structure or amino acid sequences with physics informed neural networks.
The first example evaluates the performance of different state of the art (hybrid) methods for predicting PK profiles based on chemical structures and benchmark their performance on a common data set for pre-clinical species.
Next, a method for predicting non-rodent PK profiles for using allometric AI approach is introduced. The last concept utilizes protein large language models and sequence derived features to predict in vivo clearance and PK profile in pre-clinical species.
