Lingo3DMol: Generation of a Pocket-based 3D Molecule using a Language Model

Structure-based drug design powered by deep generative models have attracted increasing research interest in recent years. Language models have demonstrated a robust capacity for generating valid molecules in 2D structures, while methods based on geometric deep learning can directly produce molecules with accurate 3D coordinates. Inspired by both methods, this article proposes a pocket-based 3D molecule generation method that leverages the language model with the ability to generate 3D coordinates. High quality protein-ligand complex data are insufficient; hence, a perturbation and restoration pre-training task is designed that can utilize vast amounts of small-molecule data. A new molecular representation, a fragment-based SMILES with local and global coordinates, is also presented, enabling the language model to learn molecular topological structures and spatial position information effectively. Ultimately, CrossDocked and DUD-E dataset is employed for evaluation and additional metrics are introduced. This method achieves state-of-the-art performance in nearly all metrics, notably in terms of binding patterns, drug-like properties, rational conformations, and inference speed. Our model is available as an online service to academic users via sw3dmg.stonewise.c

Enhancing Molecular Energy Predictions with Physically Constrained Modifications to the Neural Network Potential

Exclusively prioritizing the precision of energy prediction frequently proves inadequate in satisfying multifaceted requirements. A heightened focus is warranted on assessing the rationality of potential energy curves predicted by machine learning-based force fields (MLFF), alongside evaluating the pragmatic utility of these MLFF. This study introduces SWANI, an optimized Neural Network Potential (NNP) stemming from the ANI framework. Through the incorporation of supplementary physical constraints, SWANI aligns more cohesively with chemical expectations, yielding rational potential energy profiles. It also exhibits superior predictive precision compared to the ANI model. Additionally, a comprehensive comparison is conducted between SWANI and a prominent Graph Neural Network (GNN)-based model. The findings indicate that SWANI outperforms the latter, particularly for molecules exceeding the dimensions of the training set. This outcome underscores SWANI\u27s exceptional capacity for generalization and its proficiency in handling larger molecular systems