5 papers · 1 filter
Elite-Weighted Supervised Fine-tuning for Goal-Directed Molecular Optimization
Shiyun Wa, Yifei Wang, Anna G. Green +2
Goal-directed optimization is essential for steering molecular generators to propose candidates with desired properties. However, it is often implemented with policy-gradient reinf…
Advancing Ligand-based Virtual Screening and Molecular Generation with Pretrained Molecular Embedding Distance
Shiyun Wa, Yifei Wang, Simone Sciabola +1
Molecular similarity plays a central role in ligand-based drug discovery, such as virtual screening, analog searching, and goal-directed molecular generation. However, traditional…
Iterative Foundation Model Fine-Tuning on Multiple Rewards
Pouya M. Ghari, Simone Sciabola, Ye Wang
Fine-tuning foundation models has emerged as a powerful approach for generating objects with specific desired properties. Reinforcement learning (RL) provides an effective framewor…
ACEGEN: Reinforcement learning of generative chemical agents for drug discovery
Albert Bou, Morgan Thomas, Sebastian Dittert +10
In recent years, reinforcement learning (RL) has emerged as a valuable tool in drug design, offering the potential to propose and optimize molecules with desired properties. Howeve…
Large-scale Pretraining Improves Sample Efficiency of Active Learning based Molecule Virtual Screening
Zhonglin Cao, Simone Sciabola, Ye Wang
Virtual screening of large compound libraries to identify potential hit candidates is one of the earliest steps in drug discovery. As the size of commercially available compound co…