The history of chemistry has been epitomized by individual chemists coming up with hypotheses, running experiments at lab-scale, and producing discoveries. But in 2022, chemistry
In drug discovery, there are two main approaches to hit finding: 1) virtual screening of existing small molecule libraries and 2) generative design of new
Many of our recent blog posts have dealt with physical modeling of small molecules and proteins. This is due to a recent flurry of groundbreaking
What is an inverse problem, and where do inverse problems appear in chemistry? In deep learning for chemistry, it is common to train a classifier
We live an a world where chemistry computation is increasingly competitive with experimentation. AlphaFold predicts protein structure with accuracy sufficient for many applications. In the
Neural sequence models have recently produced astonishing results in domains ranging from natural language to proteins and biochemistry. Current sequence models trained on text can explain
For the last decade, the field of deep learning and AI has been dominated by applications to images and text. However, in the past two
Extremely data-efficient ligand generation What is a sufficient number of data points to train a deep learning algorithm? 1,000? 1 million? 1 billion? Of course,
The study of structural biochemistry is based on the axiom that “structure determines function”. A corollary of that axiom is that, for proteins, “function is
In structure-based drug discovery, most methods rely on two key elements of accuracy: accurate protein structure modeling and accurate drug structure modeling. AlphaFold is able to predict
