Scientific discovery in the age of artificial intelligence
Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment
and accelerate research, helping scientists to generate hypotheses, design experiments …
and accelerate research, helping scientists to generate hypotheses, design experiments …
Deep generative molecular design reshapes drug discovery
Recent advances and accomplishments of artificial intelligence (AI) and deep generative
models have established their usefulness in medicinal applications, especially in drug …
models have established their usefulness in medicinal applications, especially in drug …
Large language models generate functional protein sequences across diverse families
Deep-learning language models have shown promise in various biotechnological
applications, including protein design and engineering. Here we describe ProGen, a …
applications, including protein design and engineering. Here we describe ProGen, a …
Scaffolding protein functional sites using deep learning
The binding and catalytic functions of proteins are generally mediated by a small number of
functional residues held in place by the overall protein structure. Here, we describe deep …
functional residues held in place by the overall protein structure. Here, we describe deep …
Progen2: exploring the boundaries of protein language models
Attention-based models trained on protein sequences have demonstrated incredible
success at classification and generation tasks relevant for artificial-intelligence-driven …
success at classification and generation tasks relevant for artificial-intelligence-driven …
De novo protein design by deep network hallucination
There has been considerable recent progress in protein structure prediction using deep
neural networks to predict inter-residue distances from amino acid sequences,–. Here we …
neural networks to predict inter-residue distances from amino acid sequences,–. Here we …
Machine learning-guided protein engineering
Recent progress in engineering highly promising biocatalysts has increasingly involved
machine learning methods. These methods leverage existing experimental and simulation …
machine learning methods. These methods leverage existing experimental and simulation …
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
In the field of artificial intelligence, a combination of scale in data and model capacity
enabled by unsupervised learning has led to major advances in representation learning and …
enabled by unsupervised learning has led to major advances in representation learning and …
Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space
EK Makowski, PC Kinnunen, J Huang, L Wu… - Nature …, 2022 - nature.com
Therapeutic antibody development requires selection and engineering of molecules with
high affinity and other drug-like biophysical properties. Co-optimization of multiple antibody …
high affinity and other drug-like biophysical properties. Co-optimization of multiple antibody …
Machine learning-enabled retrobiosynthesis of molecules
Retrobiosynthesis provides an effective and sustainable approach to producing functional
molecules. The past few decades have witnessed a rapid expansion of biosynthetic …
molecules. The past few decades have witnessed a rapid expansion of biosynthetic …