Property prediction on molecular graphs is an important application of Graph Neural Networks (GNNs).

Thus, we propose LLaMo: L arge La nguage Model-based Mo lecular graph assistant, which is an end-to-end trained large molecular graph language model.

This broad applicability complicates their evaluation.

Molecular Property Prediction (MPP) is a critical task in computational drug discovery, aimed at identifying molecules with desirable pharmacological and ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties. Machine learning models have been widely used in this fast-growing field, with two types of models being commonly employed: traditional non-deep models and deep models.

Molecular learning is pivotal in many real-world applications, such as drug discovery.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

In particular, torsional diffusion does not address the longstanding difficulty that existing cheminformatics methods have with macrocycles--rings with 12 or more atoms that have found several applications in drug discovery [Driggers et al., 2008].