The Introductions Thread!

#83

Hi, I am Dmitriy. I am a comp chemist working on creating new scents and flavors. Thanks!

#84

Hi, I’m Shigraf. A computer science student working at a medical AI startup solving neurological diseases.
I have recently started contributing to DeepChem.

#85

Hello Everyone!
My name is Michael Ndudi, I am a software engineer and biochemistry undergrad from the University of Lagos. I am really interested in Computational Biology and hope to do a higher degree in the field. DeepChem seems super interesting and I am excited to contribute.

#86

Hello everyone,

My name is Mansi and I come from India.
I’m an upcoming master’s student in Physics and I hold a bachelor’s degree in Physics and Chemistry.

Currently, I’m doing an internship at the Computational Chemistry Lab of Dr. Rajagopala Reddy Seelam @ Central University of Rajasthan.

My interest lies at the intersection of physics and chemistry. Additionally, I have a keen interest in scientific computing and machine learning.

Looking forward to becoming a part as this year’s contributor(hopefully!)

#87

Hi everyone! I’m jahnavi, a Computer Science graduate interested in AI, ML, and open-source contributions. This is my first time contributing to open source, and I’m excited to get started with DeepChem. Looking forward to learning and contributing! lets connect.

#88

Hello I’m Amogh S. I’m an Information Science undergrad at Smvit , Bangalore. This would be my first open source contribution . The reason I’m interested in contributing in DeepChem is the aim and goal of the organization and the frameworks that are used in the project. As I already have some experience working with Pytorch, tensorflow in my previous project I’m sure I can be useful and productive in contrubuting to DeepChem. Looking forward to work and connect with the mentors and to make my useful contributions for the organization. Thank you

#89

Hi everyone! I am Naman Agarwal, currently pursuing Bachelor’s of technology in computer science and engineering at motilal nehru national institute of technology, Allahabad. I got introduced to AI ML during my 2nd year through some hackathons and my interest in those fields has only grown since. I am just a beginner when it comes to open source contributions and I am really looking forward to learn and contribute to this cause of democratizing access to deep learning tools for drug discovery

#90

Hi there!

I’m Galymzhan M, born and raised in Kazakhstan, currently PhD student in Grzybowski Lab in UNIST and CARS-IBS (Ulsan, South Korea).

I’ve been working on NN-based prediction of suitable reaction conditions for organic reactions, 3D featurization of transition metal catalyst structures, retrosynthesis prediction tools, as well as a little bit of ligand-based drug discovery. Got 2 years (and counting) experience in conducting lab team project, mentoring undergraduate students, and python package development with git version control.

Glad to join DeepChem community!

#91

Hello all! I am Diya Raghuvanshi, currently in 2nd year of my BTech in Computer science. Excited to be here!

#92

My name is Kanak Jaiswal, and I am excited about the opportunity to contribute to DeepChem through Google Summer of Code (GSoC) 2025. I am a master’s student in Artificial Intelligence with a strong background in Artificial Intelligence, Machine Learning, and computational chemistry.

I have hands-on experience with machine learning frameworks such as PyTorch and DeepChem, and I am eager to extend DeepChem’s capabilities by implementing a model that predicts the outcomes of chemical reactions. I have worked on projects involving molecular modeling and data-driven predictions in my coursework and personal projects.

Project idea-
The AI-Powered Chemical Reaction Predictor can be integrated into DeepChem as an update to enhance its capabilities in reaction prediction and retrosynthesis. This feature would involve creating a new module, reaction_prediction, which utilizes deep learning models like Graph Neural Networks (GNNs) and Transformers to predict the outcomes of chemical reactions based on reactant molecules. It will also include reaction datasets (e.g., USPTO, Reaxys) for training and testing the model. Tutorials and documentation will be provided for users to easily train and deploy these models, contributing to faster drug discovery and materials science research. This addition would further position DeepChem as a leader in scientific machine learning for chemistry.