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Data-Driven Drug Discovery and Molecular Informatics

I am a PhD student in the Bender group, in the Centre for Molecular Informatics. My research interest include the use of 3D structure data for machine (deep) learning applications in drug discovery.

In my first project, I used atomistic neural networks to bias conformer ensemble towards bioactive-like conformations.

In my current project, I will benchmark existing 3D deep molecular generation methods, both unconditioned generation (diverse conformations for diverse molecules) and conditioned generation (e.g. pocket conditioning to generate active molecule directly in target-bound poses).

I also worked on deep conformation generation in my first year, and worked on a side project exploring the use of the Smooth Overlap of Atomic Positions (SOAP) descriptor for protein-ligand prediction.

Publications

Applying atomistic neural networks to bias conformer ensembles towards bioactive-like conformations
B Baillif, J Cole, I Giangreco, P McCabe, A Bender
– Journal of Cheminformatics
(2023)
15,
124
Deep generative models for 3D molecular structure
B Baillif, J Cole, P McCabe, A Bender
– Current opinion in structural biology
(2023)
80,
102566
Exploring the Use of Compound-Induced Transcriptomic Data Generated From Cell Lines to Predict Compound Activity Toward Molecular Targets.
B Baillif, J Wichard, O Méndez-Lucio, D Rouquié
– Front Chem
(2020)
8,
296
De novo generation of hit-like molecules from gene expression signatures using artificial intelligence
O Méndez-Lucio, B Baillif, D-A Clevert, D Rouquié, J Wichard
– Nat Commun
(2020)
11,
10

Telephone number

01223 336432

Email address