activity
20212026
most citedGraphXForm: Graph transformer for computer-aided molecular design

8 citations · 19 across the 9 of their papers we have counts for

collaborators

9 papers

cs.LG2026

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…

cs.LG2026

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…

cs.LG2024★ 8 cited

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…

cs.LG2024★ 1 cited

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…

cs.LG2024★ 5 cited

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…

cs.LG2023★ 1 cited

Deep reinforcement learning uncovers processes for separating azeotropic mixtures without prior knowledge

Quirin Göttl, Jonathan Pirnay, Jakob Burger +1

Process synthesis in chemical engineering is a complex planning problem due to vast search spaces, continuous parameters and the need for generalization. Deep reinforcement learnin…

cs.LG2023★ 2 cited

Policy-Based Self-Competition for Planning Problems

Jonathan Pirnay, Quirin Göttl, Jakob Burger +1

AlphaZero-type algorithms may stop improving on single-player tasks in case the value network guiding the tree search is unable to approximate the outcome of an episode sufficientl…

physics.chem-ph2023★ 2 cited

Convex Envelope Method for determining liquid multi-phase equilibria in systems with arbitrary number of components

Quirin Göttl, Jonathan Pirnay, Dominik G. Grimm +1

The determination of liquid phase equilibria plays an important role in chemical process simulation. This work presents a generalization of an approach called the convex envelope m…

cs.CV2021

Inpainting Transformer for Anomaly Detection

Jonathan Pirnay, Keng Chai

Anomaly detection in computer vision is the task of identifying images which deviate from a set of normal images. A common approach is to train deep convolutional autoencoders to i…