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Jialu Wu

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4 papers

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AAAI Conference 2025 Conference Paper

Synergy of GFlowNet and Protein Language Model Makes a Diverse Antibody Designer

Mingze Yin
Hanjing Zhou
Yiheng Zhu
Jialu Wu
Wei Wu
Mingyang Li
Kun Fu
Zheng Wang

Antibodies defend our health by binding to antigens with high specificity and potentiality, primarily relying on the Complementarity-Determining Region (CDR). Yet, current experimental methods of discovering new antibody CDRs are heavily time-consuming. Computational design could alleviate this burden; especially, protein language models have proven quite beneficial in many recent studies. However, most existing models solely focus on antibody potentiality and struggle to encapsulate the diverse range of plausible CDR candidates, limiting their effectiveness in real-world scenarios as binding is only one factor in the multitude of drug-forming criteria. In this paper, we introduce PG-AbD, a framework uniting Generative Flow Networks (GFlowNets) and pretrained Protein Language Models (PLMs) to successfully generate highly potent, diverse and novel antibody candidates. We innovatively construct a Products of Experts (PoE) composed by the global-distribution-modeling PLM and the local-distribution-modeling Potts Model to serve as the reward function of GFlowNet. The joint training paradigm is introduced, where PoE is trained by contrastive divergence with the negative samples generated by GFlowNet, and then guides GFlowNet to sample diverse antibody candidates. We evaluate PG-AbD on extensive antibody design benchmarks. It significantly outperforms existing methods in diversity (13.5% on RabDab, 31.1% on SabDab) while maintaining optimal potential and novelty. Generated antibodies are also found to form stable, regular 3D structures with their corresponding antigens, demonstrating the great potential of PG-AbD to accelerate real-world antibody discovery.

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NeurIPS Conference 2024 Conference Paper

Bridge-IF: Learning Inverse Protein Folding with Markov Bridges

Yiheng Zhu
Jialu Wu
Qiuyi Li
Jiahuan Yan
Mingze Yin
Wei Wu
Mingyang Li
Jieping Ye

Inverse protein folding is a fundamental task in computational protein design, which aims to design protein sequences that fold into the desired backbone structures. While the development of machine learning algorithms for this task has seen significant success, the prevailing approaches, which predominantly employ a discriminative formulation, frequently encounter the error accumulation issue and often fail to capture the extensive variety of plausible sequences. To fill these gaps, we propose Bridge-IF, a generative diffusion bridge model for inverse folding, which is designed to learn the probabilistic dependency between the distributions of backbone structures and protein sequences. Specifically, we harness an expressive structure encoder to propose a discrete, informative prior derived from structures, and establish a Markov bridge to connect this prior with native sequences. During the inference stage, Bridge-IF progressively refines the prior sequence, culminating in a more plausible design. Moreover, we introduce a reparameterization perspective on Markov bridge models, from which we derive a simplified loss function that facilitates more effective training. We also modulate protein language models (PLMs) with structural conditions to precisely approximate the Markov bridge process, thereby significantly enhancing generation performance while maintaining parameter-efficient training. Extensive experiments on well-established benchmarks demonstrate that Bridge-IF predominantly surpasses existing baselines in sequence recovery and excels in the design of plausible proteins with high foldability. The code is available at https: //github. com/violet-sto/Bridge-IF.

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IJCAI Conference 2023 Conference Paper

MolHF: A Hierarchical Normalizing Flow for Molecular Graph Generation

Yiheng Zhu
Zhenqiu Ouyang
Ben Liao
Jialu Wu
Yixuan Wu
Chang-Yu Hsieh
Tingjun Hou
Jian Wu

Molecular de novo design is a critical yet challenging task in scientific fields, aiming to design novel molecular structures with desired property profiles. Significant progress has been made by resorting to generative models for graphs. However, limited attention is paid to hierarchical generative models, which can exploit the inherent hierarchical structure (with rich semantic information) of the molecular graphs and generate complex molecules of larger size that we shall demonstrate to be difficult for most existing models. The primary challenge to hierarchical generation is the non-differentiable issue caused by the generation of intermediate discrete coarsened graph structures. To sidestep this issue, we cast the tricky hierarchical generation problem over discrete spaces as the reverse process of hierarchical representation learning and propose MolHF, a new hierarchical flow-based model that generates molecular graphs in a coarse-to-fine manner. Specifically, MolHF first generates bonds through a multi-scale architecture, then generates atoms based on the coarsened graph structure at each scale. We demonstrate that MolHF achieves state-of-the-art performance in random generation and property optimization, implying its high capacity to model data distribution. Furthermore, MolHF is the first flow-based model that can be applied to model larger molecules (polymer) with more than 100 heavy atoms. The code and models are available at https: //github. com/violet-sto/MolHF.

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NeurIPS Conference 2023 Conference Paper

Sample-efficient Multi-objective Molecular Optimization with GFlowNets

Yiheng Zhu
Jialu Wu
Chaowen Hu
Jiahuan Yan
kim hsieh
Tingjun Hou
Jian Wu

Many crucial scientific problems involve designing novel molecules with desired properties, which can be formulated as a black-box optimization problem over the discrete chemical space. In practice, multiple conflicting objectives and costly evaluations (e. g. , wet-lab experiments) make the diversity of candidates paramount. Computational methods have achieved initial success but still struggle with considering diversity in both objective and search space. To fill this gap, we propose a multi-objective Bayesian optimization (MOBO) algorithm leveraging the hypernetwork-based GFlowNets (HN-GFN) as an acquisition function optimizer, with the purpose of sampling a diverse batch of candidate molecular graphs from an approximate Pareto front. Using a single preference-conditioned hypernetwork, HN-GFN learns to explore various trade-offs between objectives. We further propose a hindsight-like off-policy strategy to share high-performing molecules among different preferences in order to speed up learning for HN-GFN. We empirically illustrate that HN-GFN has adequate capacity to generalize over preferences. Moreover, experiments in various real-world MOBO settings demonstrate that our framework predominantly outperforms existing methods in terms of candidate quality and sample efficiency. The code is available at https: //github. com/violet-sto/HN-GFN.

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