GSOC’25 Project: Porting Chemception Model to PyTorch

#1

Hi everyone!
I’m Bibhusundar Mohapatra , a 3rd year undergraduate student in Biotechnology and Medical Engineering at NIT Rourkela, Odisha, India .

I’m thrilled to share that I will be working with DeepChem on the project “Porting Chemception Model to PyTorch” as part of Google Summer of Code 2025 (GSoC’25) .

Over the summer, I’ll be focusing on enhancing the interoperability and performance of the Chemception deep learning model by porting it from TensorFlow to PyTorch. I’ll be posting regular updates on my progress and would love to engage with the community throughout this journey.

Looking forward to learning, contributing, and having insightful discussions. Stay tuned for more updates!

Regards,
Bibhusundar Mohapatra

#2

Week 1 updates (june 2 - june 9)

  • Built intuition on Chemception architecture.
  • Studied the original TensorFlow implementation of ChemCeption and understood its core components.
  • Reviewed PyTorch best practices for model implementation and testing.
  • Implemented the Stem layer and got it merged into the codebase. Link to PR (https://github.com/deepchem/deepchem/pull/4286)
  • Implemented InceptionA and InceptionB layers and raised a pull request (PR).Link to PR(https://github.com/deepchem/deepchem/pull/4453)
  • Added unit tests for each implemented layer, including forward pass verification.

Link to slides

#3

Week 2 updates (Jun 9 - Jun 16)

  • Built more intuition on the ChemCeption architecture and its modular design.
  • Worked on implementing the InceptionResNetC and ReductionA layers.
  • Resolved a few issues raised in PR #4453.
  • Added corresponding unit tests for both layers, including forward pass checks.

Link to slides

#4

Week 3 updates (Jun 16 - Jun 22)

  • Built deeper intuition on ChemCeption’s Reduction layers
  • Implemented the ReductionA, ReductionB, and ReductionC layers locally with complete functionality.
  • Almost ready to raise the PR for these implementations after final review.
  • Resolved issues in PR #4453 and added unit tests for all layers, including forward pass checks.

Link to slides