Collects software dedicated to predicting specific properties of peptides
Implements the blood brain barrier score described in: J. Med. Chem. 2019, 62, 21, 9824-9836 (https://doi.org/10.1021/acs.jmedchem.9b01220)
Predictors for Blood-Brain Barrier Permeability with resampling strategies based on B3DB database.
Multi-objective generative AI for designing novel brain-targeting small molecules.
Virtual laboratory for rational drug design and discovery at the blood-brain barrier supervised by Prof. Dr. Sergey Shityakov, MD, PhD.
BBBP Explainer is a code to generate structural alerts of blood-brain barrier penetrating and non-penetrating drugs using Local Interpretable Model-Agnostic Explanations (LIME) of machine learning models from BBBP dataset.
Multi-task learning (BBB + P-gp) for blood-brain barrier permeability prediction
Antipsychotic drugs or neuroleptics are widely used in the treatment of psychosis as a manifestation of schizophrenia and bipolar disorder. However, their effectiveness largely depends on the blood-brain barrier (BBB) permeation (pharmacokinetics, PK) and drug-receptor pharmacodynamics (PD). Therefore, in this study, we developed and implemented…
Open-source computational pipeline for CNS drug-delivery formulation screening — real PBPK, DLVO colloidal stability, AutoDock Vina docking, and ChEMBL-trained QSAR, with a public audit trail of its own bugs. Research prototype, not clinically validated.
With a focus on BBB modulation, safety, and translational relevance, this academic research project investigates targeted ultrasound and microbubble-mediated approaches for improved CNS and brain tumor drug delivery.
Individual project for 4th year where models were trained to predict whether a drug can pass through the Blood-Brain Barrier using its chemical properties but also its side effects and indications. Notebooks & Streamlit App Available
Quantum Kernel Machine Learning for Drug Design A rigorous, end-to-end Qiskit implementation of quantum kernel SVMs for predicting blood-brain barrier permeability (BBBP) — a core ADMET property in CNS drug discovery — with three controlled experiments that actually test whether the quantum part is doing anything useful.
A modelisation of Blood Brain Barrier permeability through active transporters
Construct-validity audit of the standard blood–brain barrier (BBB) peptide benchmark: an identity-controlled re-evaluation + shared-source provenance/overlap map, with an open, CPU-reproducible evaluation harness. Do these predictors measure penetration, or their benchmarks?
Protocol-controlled BBB permeability benchmark: 8 architectures across random, scaffold and leakage-controlled external splits. Evaluation protocol moves the score more than architecture does.
Machine learning project for predicting blood-brain barrier permeability from molecular fingerprints.
Graph molecular learning to predict blood-brain-barrier penetration and CNS drug delivery.
Public call for an independent scientific review of blood–brain barrier and neurovascular hypotheses in Alzheimer’s disease.
A complete Graph Neural Network pipeline for drug molecule property prediction (BBBP). Features GCN, GAT, and GIN architectures, classical ML baselines, ablation studies, and model interpretability using GNNExplainer.
A graph neural network to predict blood brain barrier permeability of a drug
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