4 citations · 6 across the 9 of their papers we have counts for
10 papers
PGFS++: Molecular Property Improvement under Synthesis and Diversity Constraints
Boqiao Zhang, Godbless James, Sai Krishna Gottipati +1
Improving molecular properties, such as drug-likeness or binding affinity, is a recurring task in early-stage drug discovery. However, molecules optimized in an unconstrained chemi…
Monroe: A Molecular Foundation Model for In-Context Probabilistic Inference
Blazej Banaszewski, Andrew W. Fitzgibbon
Bioassay activity prediction is often data-limited because drug-discovery datasets rely on time-consuming and expensive wet-lab experiments for data generation and evaluation. This…
Novel Aspects of IEEE SA P3109 Arithmetic Formats for Machine Learning
Andrew Fitzgibbon, Christoph M. Wintersteiger, Jeffrey Sarnoff
The IEEE P3109 draft standard defines a parameterized family of binary floating-point formats and associated operations, with a focus on facilitating machine learning. These format…
Scalify: scale propagation for efficient low-precision LLM training
Paul Balança, Sam Hosegood, Carlo Luschi +1
Low-precision formats such as float8 have been introduced in machine learning accelerated hardware to improve computational efficiency for large language models training and infere…
MESS: Modern Electronic Structure Simulations
Hatem Helal, Andrew Fitzgibbon
Electronic structure simulation (ESS) has been used for decades to provide quantitative scientific insights on an atomistic scale, enabling advances in chemistry, biology, and mate…
: A Parameter-Efficient Foundation Model for Molecular Learning
Kerstin Kläser, Błażej Banaszewski, Samuel Maddrell-Mander +5
In biological tasks, data is rarely plentiful as it is generated from hard-to-gather measurements. Therefore, pre-training foundation models on large quantities of available data a…