Thoughts and Theory

Exploit local structure of three-dimensional molecular complexes to predict binding affinities

Nathan C. Frey

This post was co-authored by Bharath Ramsundar from DeepChem.

ACNNs learn chemical features from the three-dimensional structure of protein-ligand complexes. In this post, we show how to use the open-source implementation of ACNNs in DeepChem and the PDBbind dataset to predict protein-ligand binding affinities.

An interactive tutorial

From Unsplash

Thoughts and Theory

Towards an “ImageNet moment” in Molecular Machine Learning

This post was co-authored by Bharath Ramsundar from DeepChem.

Benchmark datasets are an important driver of progress in machine learning. Unlike computer vision and natural language processing, the diversity and complexity of datasets in chemical and life sciences make these fields largely resistant to attempts to curate benchmarks that are…

Why materials science is a mystery to most people and why it’s important

When I (used to) get a haircut or take an Uber, the #1 question I got is the same one everyone else gets: “What do you do?” If I’m feeling really adventurous, I might say “I’m a…

How to get started doing research in materials informatics (data science + materials science)

In this post I share resources and recommendations for getting involved in materials informatics research. As it becomes increasingly more expensive and time-consuming to discover and engineer new materials to address some of our most pressing global challenges (human health, food and water security, climate, etc.), we need materials scientists…

Searching for materials with multiple quantum properties

What is a MOM? Patient, supportive, loving…no, not quite, in the cold world of materials physics, a MOM is a multi-order material. That means a material with two or more quantum “orders,” where electrons in the material are organized in some way because…

How semi-supervised learning is used to accelerate materials synthesis

Nathan C. Frey

This post was co-authored with Vishnu Harshith from IIT Madras

Many real-world problems involve datasets where only some of the data is labeled and the rest is unlabeled. In this post, we discuss our implementation of semi-supervised learning for predicting the synthesizability of theoretical materials.

When we…

Nathan C. Frey, PhD

Postdoc @MIT | Co-founder @AtomicAI | Previously NDSEG Fellow @UPenn and affiliate scientist @Berkeley Lab

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