Attending to graph transformers
Recently, transformer architectures for graphs emerged as an alternative to established
techniques for machine learning with graphs, such as (message-passing) graph neural …
techniques for machine learning with graphs, such as (message-passing) graph neural …
Artificial intelligence for science in quantum, atomistic, and continuum systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …
Data-driven quantum chemical property prediction leveraging 3d conformations with uni-mol+
Quantum chemical (QC) property prediction is crucial for computational materials and drug
design, but relies on expensive electronic structure calculations like density functional theory …
design, but relies on expensive electronic structure calculations like density functional theory …
Coati: Multimodal contrastive pretraining for representing and traversing chemical space
B Kaufman, EC Williams, C Underkoffler… - Journal of Chemical …, 2024 - ACS Publications
Creating a successful small molecule drug is a challenging multiparameter optimization
problem in an effectively infinite space of possible molecules. Generative models have …
problem in an effectively infinite space of possible molecules. Generative models have …
Generating QM1B with PySCF
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 …
have resulted in immense progress on downstream tasks. This progress was enabled by …
Impact of Domain Knowledge and Multi-Modality on Intelligent Molecular Property Prediction: A Systematic Survey
The precise prediction of molecular properties is essential for advancements in drug
development, particularly in virtual screening and compound optimization. The recent …
development, particularly in virtual screening and compound optimization. The recent …
Automated 3D pre-training for molecular property prediction
Molecular property prediction is an important problem in drug discovery and materials
science. As geometric structures have been demonstrated necessary for molecular property …
science. As geometric structures have been demonstrated necessary for molecular property …
GeoMFormer: A general architecture for geometric molecular representation learning
Molecular modeling, a central topic in quantum mechanics, aims to accurately calculate the
properties and simulate the behaviors of molecular systems. The molecular model is …
properties and simulate the behaviors of molecular systems. The molecular model is …
Highly accurate quantum chemical property prediction with uni-mol+
Recent developments in deep learning have made remarkable progress in speeding up the
prediction of quantum chemical (QC) properties by removing the need for expensive …
prediction of quantum chemical (QC) properties by removing the need for expensive …
Application of Transformers in Cheminformatics
KD Luong, A Singh - Journal of Chemical Information and …, 2024 - ACS Publications
By accelerating time-consuming processes with high efficiency, computing has become an
essential part of many modern chemical pipelines. Machine learning is a class of computing …
essential part of many modern chemical pipelines. Machine learning is a class of computing …