7 citations · 13 across the 7 of their papers we have counts for
10 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…
Ensemble-Conditioned Molecular Design
Ross Irwin, Alessandro Tibo, Jon Paul Janet +1
Molecular design is typically approached as a problem of finding molecules which can adopt a single bioactive conformation. In reality, molecules occupy a distribution over conform…
Uncertainty-aware reinforcement learning for chemical language models
Borja Medina, Jon Paul Janet
Reinforcement Learning (RL) has become a powerful paradigm for de novo molecular design, enabling Chemical Language Models (CLMs) to navigate and explore the chemical space while o…
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…
Assay2Mol: large language model-based drug design using BioAssay context
Yifan Deng, Spencer S. Ericksen, Anthony Gitter
Scientific databases aggregate vast amounts of quantitative data alongside descriptive text. In biochemistry, molecule screening assays evaluate candidate molecules' functional res…
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…
FLOWR: Flow Matching for Structure-Aware De Novo, Interaction- and Fragment-Based Ligand Generation
Julian Cremer, Ross Irwin, Alessandro Tibo +3
We introduce FLOWR, a novel structure-based framework for the generation and optimization of three-dimensional ligands. FLOWR integrates continuous and categorical flow matching wi…
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…
SemlaFlow -- Efficient 3D Molecular Generation with Latent Attention and Equivariant Flow Matching
Ross Irwin, Alessandro Tibo, Jon Paul Janet +1
Methods for jointly generating molecular graphs along with their 3D conformations have gained prominence recently due to their potential impact on structure-based drug design. Curr…
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…