[HTML][HTML] Adaptive machine learning for protein engineering
Abstract Machine-learning models that learn from data to predict how protein sequence
encodes function are emerging as a useful protein engineering tool. However, when using …
encodes function are emerging as a useful protein engineering tool. However, when using …
In vitro continuous protein evolution empowered by machine learning and automation
Directed evolution has become one of the most successful and powerful tools for protein
engineering. However, the efforts required for designing, constructing, and screening a large …
engineering. However, the efforts required for designing, constructing, and screening a large …
Biological sequence design with gflownets
Abstract Design of de novo biological sequences with desired properties, like protein and
DNA sequences, often involves an active loop with several rounds of molecule ideation and …
DNA sequences, often involves an active loop with several rounds of molecule ideation and …
Sample efficiency matters: a benchmark for practical molecular optimization
Molecular optimization is a fundamental goal in the chemical sciences and is of central
interest to drug and material design. In recent years, significant progress has been made in …
interest to drug and material design. In recent years, significant progress has been made in …
Accelerating bayesian optimization for biological sequence design with denoising autoencoders
Bayesian optimization (BayesOpt) is a gold standard for query-efficient continuous
optimization. However, its adoption for drug design has been hindered by the discrete, high …
optimization. However, its adoption for drug design has been hindered by the discrete, high …
Gflownets for ai-driven scientific discovery
Tackling the most pressing problems for humanity, such as the climate crisis and the threat
of global pandemics, requires accelerating the pace of scientific discovery. While science …
of global pandemics, requires accelerating the pace of scientific discovery. While science …
Reinforced genetic algorithm for structure-based drug design
Abstract Structure-based drug design (SBDD) aims to discover drug candidates by finding
molecules (ligands) that bind tightly to a disease-related protein (targets), which is the …
molecules (ligands) that bind tightly to a disease-related protein (targets), which is the …
Differentiable scaffolding tree for molecular optimization
The structural design of functional molecules, also called molecular optimization, is an
essential chemical science and engineering task with important applications, such as drug …
essential chemical science and engineering task with important applications, such as drug …
Toward real-world automated antibody design with combinatorial Bayesian optimization
Antibodies are multimeric proteins capable of highly specific molecular recognition. The
complementarity determining region 3 of the antibody variable heavy chain (CDRH3) often …
complementarity determining region 3 of the antibody variable heavy chain (CDRH3) often …
Group SELFIES: a robust fragment-based molecular string representation
We introduce Group SELFIES, a molecular string representation that leverages group tokens
to represent functional groups or entire substructures while maintaining chemical robustness …
to represent functional groups or entire substructures while maintaining chemical robustness …