Generative language models define distributions over sequences of tokens that can
represent essentially any combination of data modalities (eg, any permutation of image …

Automating retrosynthesis with artificial intelligence expedites organic chemistry research in
digital laboratories. However, most existing deep-learning approaches are hard to explain …

Activity and property prediction models are the central workhorses in drug discovery and
materials sciences, but currently, they have to be trained or fine-tuned for new tasks. Without …

Large Language Models (LLMs) have emerged as a transformative power in enhancing
natural language comprehension, representing a significant stride toward artificial general …

Large language models (LLMs) are able to do accurate classification with zero or only a few
examples (in-context learning). We show a prompting system that enables regression with …

Creating a successful small molecule drug is a challenging multiparameter optimization
problem in an effectively infinite space of possible molecules. Generative models have …

We demonstrate the ability of large language models (LLMs) to perform material and
molecular property regression tasks, a significant deviation from the conventional LLM use …

In many scientific fields, large language models (LLMs) have revolutionized the way with
which text and other modalities of data (eg, molecules and proteins) are dealt, achieving …

The recent integration of chemistry with natural language processing (NLP) has advanced
drug discovery. Molecule representation in language models (LMs) is crucial in enhancing …

Motivation The present paradigm of deep learning models for molecular representation
relies mostly on 1D or 2D formats, neglecting significant 3D structural information that offers …