arXiv subjects

Zongying Lin

Publications and source records attributed to Zongying Lin.

4 recordsLinked to original sources

arXiv2024

Navigating Chemical-Linguistic Sharing Space with Heterogeneous Molecular Encoding

Chemical language models (CLMs) are prominent for their effectiveness in exploring chemical space and enabling molecular engineering. However, while exploring chemical-linguistic space, CLMs suffer from the gap between natural language and molecular representations. This challenge is primarily due to the inherent modeling differences between molecules and texts: molecules operate unified modeling to learn chemical space, while natural language sequentially models the semantic space. Additionally, the limited availability of high-quality text-to-molecule datasets further exacerbates this challenge. To address the problem, we first verified the information bias in molecular representations from different perspectives. We then developed the Heterogeneous Molecular Encoding (HME) framework, a unified molecular encoder compressing the molecular features from fragment sequence, topology, and conformation with Q-learning. To better model chemical-linguistic space, we further constructed the MCMoD dataset, which contains over one million molecules with various conditions, including properties, fragments, and descriptions. Experimentally, HME promotes CLMs to achieve chemical-linguistic sharing space exploration: (1) chemical space exploration with linguistic guidance, where HME achieves significant improvements (+8.9\% FCD) for molecular design in multiple constraints, even in zero-shot scenarios; (2) linguistic space exploration with molecular guidance, where HME generates textual descriptions with high qualities (+11.6\% BLEU) for molecules. These results highlight the precision of HME in handling multi-objective and cross-domain tasks, as well as its remarkable generalization capability on unseen task combinations. HME offers a new perspective on navigating chemical-linguistic sharing space, advancing the potential of CLMs in both fundamental research and practical applications in chemistry.

cs.CE↗

arXiv2024

CASA: Class-Agnostic Shared Attributes in Vision-Language Models for Efficient Incremental Object Detection

Incremental object detection is fundamentally challenged by catastrophic forgetting. A major factor contributing to this issue is background shift, where background categories in sequential tasks may overlap with either previously learned or future unseen classes. To address this, we propose a novel method called Class-Agnostic Shared Attribute Base (CASA) that encourages the model to learn category-agnostic attributes shared across incremental classes. Our approach leverages an LLM to generate candidate textual attributes, selects the most relevant ones based on the current training data, and records their importance in an assignment matrix. For subsequent tasks, the retained attributes are frozen, and new attributes are selected from the remaining candidates, ensuring both knowledge retention and adaptability. Extensive experiments on the COCO dataset demonstrate the state-of-the-art performance of our method.

cs.CV↗

arXiv2024

Multi-granularity Score-based Generative Framework Enables Efficient Inverse Design of Complex Organics

Efficiently retrieving an enormous chemical library to design targeted molecules is crucial for accelerating drug discovery, organic chemistry, and optoelectronic materials. Despite the emergence of generative models to produce novel drug-like molecules, in a more realistic scenario, the complexity of functional groups (e.g., pyrene, acenaphthylene, and bridged-ring systems) and extensive molecular scaffolds remain challenging obstacles for the generation of complex organics. Traditionally, the former demands an extra learning process, e.g., molecular pre-training, and the latter requires expensive computational resources. To address these challenges, we propose OrgMol-Design, a multi-granularity framework for efficiently designing complex organics. Our OrgMol-Design is composed of a score-based generative model via fragment prior for diverse coarse-grained scaffold generation and a chemical-rule-aware scoring model for fine-grained molecular structure design, circumventing the difficulty of intricate substructure learning without losing connection details among fragments. Our approach achieves state-of-the-art performance in four real-world and more challenging benchmarks covering broader scientific domains, outperforming advanced molecule generative models. Additionally, it delivers a substantial speedup and graphics memory reduction compared to diffusion-based graph models. Our results also demonstrate the importance of leveraging fragment prior for a generalized molecule inverse design model.

cs.CE↗

arXiv2024

ProLLaMA: A Protein Large Language Model for Multi-Task Protein Language Processing

Recent advances in Protein Language Models (PLMs) have transformed protein engineering, yet unlike their counterparts in Natural Language Processing (NLP), current PLMs exhibit a fundamental limitation: they excel in either Protein Language Understanding (PLU) or Protein Language Generation (PLG), but rarely both. This fragmentation hinders progress in protein engineering. To bridge this gap, we introduce ProLLaMA, a multitask protein language model enhanced by the Evolutionary Protein Generation Framework (EPGF). We construct a comprehensive instruction dataset containing approximately 13 million samples with over 11,000 superfamily annotations to facilitate better modeling of sequence-function landscapes. We leverage a two-stage training approach to develop ProLLaMA, a multitask LLM with protein domain expertise. Our EPGF addresses the mismatch between statistic language modeling and biological constraints through three innovations: a multi-dimensional interpretable scorer, hierarchical efficient decoding, and a probabilistic-biophysical joint selection mechanism. Extensive experiments demonstrate that ProLLaMA excels in both unconditional and controllable protein generation tasks, achieving superior structural quality metrics compared to existing PLMs. Additionally, ProLLaMA demonstrates strong understanding capabilities with a 67.1% exact match rate in superfamily prediction. EPGF significantly enhances the biological viability of generated sequences, as evidenced by improved biophysical scores (+4.3%) and structural metrics (+14.5%). The project is available at https://github.com/PKU-YuanGroup/ProLLaMA.

cs.CE↗