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Paul Join-Lambert

dblp:410/7290 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Machine learning › Generative modeling
flow matching
0.912025
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
0.912025
Template-Guided 3D Molecular Pose Generation via Flow Matching and Differentiable Optimization · NeurIPS 2025
Mathematical optimization › optimization for machine learning
differentiable optimization
0.912025
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
YearPublicationVenuePosition
2025 Template-Guided 3D Molecular Pose Generation via Flow Matching and Differentiable Optimization
abstract
Predicting 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
NeurIPS3