Part Three
By Cameron Webster, Jillian Green, Chris Rohlicek, and Akshay Shah

We are graduate students in the Data Science Master’s program at Brown University. As part of our Deep Learning and Special Topics in Data Science course (Data 2040), we worked on processing molecular compounds using sequence models.

Introduction

Some of the greatest technological strides that have been made recently in machine learning research have been in the areas of natural language processing. These advances have come in the form of technologies like transformers, attention mechanisms, and context-based encoding schemes to learn high-level representations for the abstract concepts conveyed in…


Part Two
By Cameron Webster, Jillian Green, Chris Rohlicek, and Akshay Shah

We are graduate students in the Data Science Master’s program at Brown University. As part of our Deep Learning and Special Topics in Data Science course (Data 2040), we worked on processing molecular compounds using sequence models.

A Brief Recap

In Blog 1 we discussed the dataset, EDA, and our baseline model. Our goal of this project is to come up with innovative ways of encoding SMILES molecular compound representations as sequences for predicting toxicity. The data consists of string representations of molecules written in a format used to encode the…


Part One
By Cameron Webster, Jillian Green, Chris Rohlicek, and Akshay Shah

We are graduate students in the Data Science Master’s program at Brown University. As part of our Deep Learning and Special Topics in Data Science course (Data 2040), we worked on processing molecular compounds using sequence models.

Project Overview

Chemistry is a field that has recently experienced a great surge of research attention from the machine learning community. This crossover between disciplines has been spurred by the fact that many of the modern text-processing techniques that have been developed in machine learning are very useful in extracting information from chemical…

2040 Final Project

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