50 citations · 52 across the 3 of their papers we have counts for
5 papers
Information-Dense Synthesis for Molecular Discovery
Kasper K. Jakobsen, Eli N. Weinstein
Machine learning can accelerate molecular discovery by designing molecules and planning experiments. However, many scientific challenges demand molecules with very rare properties,…
Continuous Variational Synthesis
Alan N. Amin, Mattia G. Gollub, Andrei Slabodkin +2
Biological machine learning was long bottlenecked by the ability to synthesize designed DNA. Variational synthesis models control chemical reactions to physically manufacture quadr…
Biological Sequence Kernels with Guaranteed Flexibility
Alan Nawzad Amin, Eli Nathan Weinstein, Debora Susan Marks
Applying machine learning to biological sequences - DNA, RNA and protein - has enormous potential to advance human health, environmental sustainability, and fundamental biological…
ProGen2: Exploring the Boundaries of Protein Language Models
Erik Nijkamp, Jeffrey Ruffolo, Eli N. Weinstein +2
Attention-based models trained on protein sequences have demonstrated incredible success at classification and generation tasks relevant for artificial intelligence-driven protein…
Bayesian data selection
Eli N. Weinstein, Jeffrey W. Miller
Insights into complex, high-dimensional data can be obtained by discovering features of the data that match or do not match a model of interest. To formalize this task, we introduc…