Advances in Molecular Design and Generation

The field of molecular design and generation is rapidly advancing, with a focus on developing innovative methods for generating and optimizing molecular structures. Recent developments have highlighted the potential of large language models, graph neural networks, and generative models for advancing the field. Notably, the integration of large language models with symbolic planning has shown promise in enhancing cognitive robotics, while graph neural networks have been successfully applied to molecular property prediction and generation tasks. Furthermore, generative models have been used to generate novel molecular structures with desired properties, such as drug-like molecules and metal-organic frameworks. These advancements have the potential to accelerate drug discovery and materials science research.

Noteworthy papers in this area include Efficient and Programmable Exploration of Synthesizable Chemical Space, which presents a novel approach for molecular discovery within synthesizable chemical space, and Mofasa, which introduces a state-of-the-art generative model for generating metal-organic frameworks. Additionally, Graph VQ-Transformer and VoxCap are also noteworthy for their innovative approaches to molecular generation and design.

Sources

Enhancing Cognitive Robotics with Commonsense through LLM-Generated Preconditions and Subgoals

Efficient and Programmable Exploration of Synthesizable Chemical Space

Rep3Net: An Approach Exploiting Multimodal Representation for Molecular Bioactivity Prediction

Generalized Graph Transformer Variational Autoencoder

Towards Precision Protein-Ligand Affinity Prediction Benchmark: A Complete and Modification-Aware DAVIS Dataset

SynthStrategy: Extracting and Formalizing Latent Strategic Insights from LLMs in Organic Chemistry

LLM-Driven Multi-Agent Curation and Expansion of Metal-Organic Frameworks Database

Mofasa: A Step Change in Metal-Organic Framework Generation

Pharmacophore-based design by learning on voxel grids

Graph VQ-Transformer (GVT): Fast and Accurate Molecular Generation via High-Fidelity Discrete Latents

Accumulated Local Effects and Graph Neural Networks for link prediction

Atomic Diffusion Models for Small Molecule Structure Elucidation from NMR Spectra

Fine-Tuning ChemBERTa for Predicting Inhibitory Activity Against TDP1 Using Deep Learning

GraphBench: Next-generation graph learning benchmarking

BioMedGPT-Mol: Multi-task Learning for Molecular Understanding and Generation

OMTRA: A Multi-Task Generative Model for Structure-Based Drug Design