Furthermore, we explore the potential of language models in generating co-crystals. Finally, we present numerous previously unknown co-crystals predicted by GEM-CODE and discuss its potential in accelerating drug development.

These profiles are fed back to the LLM, which then revises the code to reduce overhead.

Topic models have been evolving rapidly over the years, from conventional to recent neural models.

Empirical insights into MLPs are important for multiple reasons.

Ideally, we would like methods that train LMs only once ( i.e., one-shot) with a fixed objective, as in