Deepchem Open Source Fellow (funded by Deep Forest Sciences)

Hi everyone, I am Anshuman Mishra.I’ll be contributing for Deepchem under Deepchem Open Source Fellowship funded by Deep Forest Sciences.

About Me

I am in Prefinal year of my Bachelor of Technology, in Electronics & Communication Engineering at National Insitute of Technology, Warangal, India. I’ve always been interested in Machine Learning. On a side note, Chemistry has been my favorite subject since school. Thus, in Deepchem, I found perfect blend of these two. As a result, I have kept myself involved with Deepchem Community since February, 2022.


According to this post on the Forum, “Growing support for Neural ODEs and CFD is on top priority list for Deepchem ‘’. The Convolutional ODE model to be contributed through this project in the original paper was used to model turbulent fluid flow by using Neural ODE. Thus it is an ideal choice to start with, for Neural ODE models for deepchem.

A Neural ODE which can learn the ‘equation’ or system dynamics to be more precise, by being fed data can be extremely powerful in not just medicine, but biology, chemistry, physics and engineering too. The conjoining of dynamical systems and deep learning has become a topic of great interest.
The project proposal can be found here .


Email :



Week 0 Updates ( June, 12 - 18 )

PR : #2911

Have been actively working on the Encoder layer for the Conv OPE

Had 2 Code Reviews with @arunppsg and @ScienceStanley

Primarily worked with improving code quality and making the model more flexible (especially user being able to provide layer size, kernel size, stride size, dropout for each layer.

Discussed in Office hours to rather porting CNN tf implementation to PyTorch and use that as encoder in the ODE model would be more meaningful contribution to Deepchem.

Next Step

  1. Tweaks in current code to convert it into PyTorch ported model of tf cnn of Deepchem
  2. Resolving type issues, [CI failing]
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Working on PR :
#2951 #2952 #2953

Have been working parallely on porting Deepchem’s Keras implementation of CNN to PyTorch
Started working on ODELayer, Dynamics Layers
Got valuable review from Bharath on focusing on CNN and get it merged in

Experimenting with CNN to reuse the layer by introducing an extra parameter ‘transpose’ used as a switch between ConvNd and ConvTransposeNd, for Encoder, Decoder as well as ODELayer : Colab,

Up Next :
CNN Layer tests : overfitting, save-reload etc,
Torchmodel wrapper for CNN

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