6 papers · 1 filter
Self-Improvement Imitation with Biologically Guided Search for Protein Design Under Oracle Budgets
Ashima Khanna, Dominik Grimm
Protein sequence optimization under tight oracle budgets requires methods that explore vast combinatorial spaces while making each evaluation informative. Existing reinforcement le…
Amortized Molecular Optimization via Group Relative Policy Optimization
Muhammad bin Javaid, Hasham Hussain, Ashima Khanna +5
In structurally constrained molecular optimization, state-of-the-art methods restart an expensive oracle-driven search from scratch for every new input structure, scaling poorly to…
Sample Efficient Generative Molecular Optimization with Joint Self-Improvement
Serra Korkmaz, Adam Izdebski, Jonathan Pirnay +5
Generative molecular optimization aims to design molecules with properties surpassing those of existing compounds. However, such candidates are rare and expensive to evaluate, yiel…
GraphXForm: Graph transformer for computer-aided molecular design
Jonathan Pirnay, Jan G. Rittig, Alexander B. Wolf +4
Generative deep learning has become pivotal in molecular design for drug discovery, materials science, and chemical engineering. A widely used paradigm is to pretrain neural networ…
Take a Step and Reconsider: Sequence Decoding for Self-Improved Neural Combinatorial Optimization
Jonathan Pirnay, Dominik G. Grimm
The constructive approach within Neural Combinatorial Optimization (NCO) treats a combinatorial optimization problem as a finite Markov decision process, where solutions are built…
Self-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but Improvement
Jonathan Pirnay, Dominik G. Grimm
Current methods for end-to-end constructive neural combinatorial optimization usually train a policy using behavior cloning from expert solutions or policy gradient methods from re…