Demo Data from EDBT 2026 / reviewers in the wild / expert
Paul Join-Lambert
dblp:410/7290
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Generative modeling · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% | |
| Theoretical computer science
1 paper |
Mathematical optimization · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Generative modeling
flow matching |
0.9 | 1 | 2025 | Template-Guided 3D Molecular Pose Generation via Flow Matching and Differentiable Optimization · NeurIPS 2025 |
Bioinformatics and computational biology › structural bioinformatics › molecular structure prediction
molecular conformation prediction |
0.9 | 1 | 2025 | Template-Guided 3D Molecular Pose Generation via Flow Matching and Differentiable Optimization · NeurIPS 2025 |
Mathematical optimization › optimization for machine learning
differentiable optimization |
0.9 | 1 | 2025 | Template-Guided 3D Molecular Pose Generation via Flow Matching and Differentiable Optimization · NeurIPS 2025 |
Methods — techniques the papers use, named apart from their topics
pharmacophore similarity · 2.6flow matching · 2.6differentiable optimization · 2.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Template-Guided 3D Molecular Pose Generation via Flow Matching and Differentiable OptimizationabstractPredicting the 3D conformation of small molecules within protein binding sites is a key challenge in drug design. When a crystallized reference ligand (template) is available, it provides geometric priors that can guide 3D pose prediction. We present a two-stage method for ligand conformation generation guided by such templates. In the first stage, we introduce a molecular alignment approach based on flow-matching to generate 3D coordinates for the ligand, using the template structure as a reference. In the second stage, a differentiable pose optimization procedure refines this conformation based on shape and pharmacophore similarities, internal energy, and, optionally, the protein binding pocket. We introduce a new benchmark of ligand pairs co-crystallized with the same target to evaluate our approach and show that it outperforms standard docking tools and open-access alignment methods, especially in cases involving low similarity to the template or high ligand flexibility. Noémie Bergues, Arthur Carré, Paul Join-Lambert, Brice Hoffmann, Arnaud Blondel, Hamza Tajmouati |
NeurIPS | 3 |