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…
Reducing the Cost of Quantum Chemical Data By Backpropagating Through Density Functional Theory
Alexander Mathiasen, Hatem Helal, Paul Balanca +6
Density Functional Theory (DFT) accurately predicts the quantum chemical properties of molecules, but scales as . Schütt et al. (2019) successfully appro…
Generating QM1B with PySCF
Alexander Mathiasen, Hatem Helal, Kerstin Klaser +6
The emergence of foundation models in Computer Vision and Natural Language Processing have resulted in immense progress on downstream tasks. This progress was enabled by datasets w…
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Dominique Beaini, Shenyang Huang, Joao Alex Cunha +32
Recently, pre-trained foundation models have enabled significant advancements in multiple fields. In molecular machine learning, however, where datasets are often hand-curated, and…
Training and inference of large language models using 8-bit floating point
Sergio P. Perez, Yan Zhang, James Briggs +6
FP8 formats are gaining popularity to boost the computational efficiency for training and inference of large deep learning models. Their main challenge is that a careful choice of…