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Found 5 papers, 4 papers with code
Learning to Discover Medicines
Discovering new medicines is the hallmark of human endeavor to live a better and longer life.
Mitigating cold start problems in drug-target affinity prediction with interaction knowledge transferring
While the drug or target representation can be learned in an unsupervised manner, it still lacks the interaction information, which is critical in drug-target interaction.
Counterfactual Explanation with Multi-Agent Reinforcement Learning for Drug Target Prediction
Methods: We propose a multi-agent reinforcement learning framework, Multi-Agent Counterfactual Drug target binding Affinity (MACDA), to generate counterfactual explanations for the drug-protein complex.
GEFA: Early Fusion Approach in Drug-Target Affinity Prediction
In addition, previous DTA methods learn protein representation solely based on a small number of protein sequences in DTA datasets while neglecting the use of proteins outside of the DTA datasets.
UAV Trajectory Planning for Data Collection from Time-Constrained IoT Devices
To this end, we jointly optimize the trajectory of a UAV and the radio resource allocation to maximize the number of served IoT devices, where each device has its own target data upload deadline.
