2 citations · 3 across the 7 of their papers we have counts for
9 papers
Can LLMs Reliably Annotate Bioassay Metadata to Improve Data Readiness?
Laura van Weesep, Riccardo Tedoldi, Jens Sjölund +6
The emergence of foundation models for molecular property prediction requires a high degree of AI data readiness, including reliable metadata annotation. However, both public repos…
Confidence is the key: how conformal prediction enhances the generative design of permeable peptides
Laura van Weesep, Sunay Chankeshwara, Leonardo De Maria +3
Generative models coupled with reinforcement learning (RL), such as REINVENT and PepINVENT, have emerged as a powerful framework for de novo molecular design. During the ideation p…
FlexiFlow: decomposable flow matching for generation of flexible molecular ensemble
Riccardo Tedoldi, Ola Engkvist, Patrick Bryant +3
Sampling useful three-dimensional molecular structures along with their most favorable conformations is a key challenge in drug discovery. Current state-of-the-art 3D de-novo desig…
Exploring Modularity of Agentic Systems for Drug Discovery
Laura van Weesep, Samuel Genheden, Ola Engkvist +1
Large-language models (LLMs) and agentic systems present exciting opportunities to accelerate drug discovery. In this study, we examine the modularity of LLM-based agentic systems…
Diverse Mini-Batch Selection in Reinforcement Learning for Efficient Chemical Exploration in de novo Drug Design
Hampus Gummesson Svensson, Ola Engkvist, Jon Paul Janet +2
In many real-world applications, evaluating the quality of instances is costly and time-consuming, e.g., human feedback and physics simulations, in contrast to proposing new instan…
Boltzmann priors for Implicit Transfer Operators
Juan Viguera Diez, Mathias Schreiner, Ola Engkvist +1
Accurate prediction of thermodynamic properties is essential in drug discovery and materials science. Molecular dynamics (MD) simulations provide a principled approach to this task…
Diversity-Aware Reinforcement Learning for de novo Drug Design
Hampus Gummesson Svensson, Christian Tyrchan, Ola Engkvist +1
Fine-tuning a pre-trained generative model has demonstrated good performance in generating promising drug molecules. The fine-tuning task is often formulated as a reinforcement lea…
PepINVENT: Generative peptide design beyond the natural amino acids
Gökçe Geylan, Jon Paul Janet, Alessandro Tibo +7
Peptides play a crucial role in the drug design and discovery whether as a therapeutic modality or a delivery agent. Non-natural amino acids (NNAAs) have been used to enhance the p…
Navigating the Maize: Cyclic and conditional computational graphs for molecular simulation
Thomas Löhr, Michele Assante, Michael Dodds +5
Many computational chemistry and molecular simulation workflows can be expressed as graphs. This abstraction is useful to modularize and potentially reuse existing components, as w…