Programming assignments covering fundamentals of machine learning and deep learning. These were completed as part of the Plaksha Tech Leaders Fellowship program.
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Updated
Aug 7, 2021 - Jupyter Notebook
Programming assignments covering fundamentals of machine learning and deep learning. These were completed as part of the Plaksha Tech Leaders Fellowship program.
RGCVAE: Relational Graph Conditioned Variational Autoencoder for Molecule Design
Hierarchical generative and regressive machine learning for next generation materials screening
GGPM - GraphNN Generation of Organic Photovoltaic Molecules
MOLER: Incorporate Molecule-Level Reward to Enhance Deep Generative Model for Molecule Optimization
exploring latent space of MoLeR AI for drug discovery
Higher-order permutation-equivariant graph variational autoencoder to generate molecules in multiresolution manner. https://iopscience.iop.org/article/10.1088/2632-2153/acc0d8
OM-Diff: Inverse-design of organometallic catalysts with guided equivariant denoising diffusion
Demo application showcasing WarmMolGen Models' generation capabilities.
Adversarial Learned Molecular Graph Inference and Generation
[ICLR 2024] Protein-Ligand Interaction Prior for Binding-aware 3D Molecule Diffusion Models
The implementation, training and evaluation of a Structure Seer machine learning model designed for reconstruction of adjacency of a molecular graph from the labelling of its nodes.
Combinatorial Complex Score-based Diffusion model using stochastic differential equations
[NeurIPS'23] Source code of "Data-Centric Learning from Unlabeled Graphs with Diffusion Model": A data-centric transfer learning framework with diffusion model on graphs.
Comparisons of Drug Generation Models
[ICLR 2024] Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for Molecule Generation
Conditional Constrained Graph Variational Autoencoders (CCGVAE) for Molecule Design
Multiresolution Equivariant Graph Variational Autoencoder (MGVAE) https://arxiv.org/abs/2106.00967
Code for the paper "Exploiting Pretrained Biochemical Language Models for Targeted Drug Design", to appear in Bioinformatics, Proceedings of ECCB2022.
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