nagham-osman
PhD student at University College London. Main interests: Graph Generative Models (particularly Diffusion Models).
University College London (UCL)London, United Kingdom
Pinned Repositories
AMLS_II
This code is for use within the UCL Electronic Engineering AMLS II module (ELEC0135).
AMLS_II
This code is for use within the UCL Electronic Engineering AMLS II module (ELEC0135).
Deep_Understanding_of_AI_Based_Drug_Discovery
The present study is finalised to determine the most advanced models in the literature capable of producing new high-quality molecules starting from well-known datasets. The selection is carried out through a series of evaluation processes. At first, the output samples of each method are evaluated according to certain physico-chemical properties such as Quantitative Estimation of Drug-likeness (QED) and Synthetic Accessibility (SA). Then, in a successive step, the assessment also includes the predicted activity towards one target protein. The final aim of the project actually is to better understand whether and how the performance of each model varies when the typology of the target protein is changed. The modified code used to run the models is provided in the GitHub repo provided in description.
helx
Interoperating between (Deep) Reiforcement Learning libraries
MiDi
MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation
nagham-osman.github.io
rdkit
The official sources for the RDKit library
DSBConnectivity
nagham-osman's Repositories
nagham-osman/AMLS_II
This code is for use within the UCL Electronic Engineering AMLS II module (ELEC0135).
nagham-osman/Deep_Understanding_of_AI_Based_Drug_Discovery
The present study is finalised to determine the most advanced models in the literature capable of producing new high-quality molecules starting from well-known datasets. The selection is carried out through a series of evaluation processes. At first, the output samples of each method are evaluated according to certain physico-chemical properties such as Quantitative Estimation of Drug-likeness (QED) and Synthetic Accessibility (SA). Then, in a successive step, the assessment also includes the predicted activity towards one target protein. The final aim of the project actually is to better understand whether and how the performance of each model varies when the typology of the target protein is changed. The modified code used to run the models is provided in the GitHub repo provided in description.
nagham-osman/helx
Interoperating between (Deep) Reiforcement Learning libraries
nagham-osman/MiDi
MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation
nagham-osman/nagham-osman.github.io
nagham-osman/rdkit
The official sources for the RDKit library