Jinbo Xu

ICML Conference 2025 Conference Paper

Retrieval Augmented Zero-Shot Enzyme Generation for Specified Substrate

• Jiahe Du
• Kaixiong Zhou
• Xinyu Hong
• Zhaozhuo Xu
• Jinbo Xu
• Xiao Huang 0001

Generating novel enzymes for target molecules in zero-shot scenarios is a fundamental challenge in biomaterial synthesis and chemical production. Without known enzymes for a target molecule, training generative models becomes difficult due to the lack of direct supervision. To address this, we propose a retrieval-augmented generation method that uses existing enzyme-substrate data to guide enzyme design. Our method retrieves enzymes with substrates that share structural similarities with the target molecule, leveraging functional similarities in catalytic activity. Since none of the retrieved enzymes directly catalyze the target molecule, we use a conditioned discrete diffusion model to generate new enzymes based on the retrieved examples. An enzyme-substrate relationship classifier guides the generation process to ensure optimal protein sequence distributions. We evaluate our model on enzyme design tasks with diverse real-world substrates and show that it outperforms existing protein generation methods in catalytic capability, foldability, and docking accuracy. Additionally, we define the zero-shot substrate-specified enzyme generation task and introduce a dataset with evaluation benchmarks.

UAI Conference 2021 Conference Paper

PALM: Probabilistic area loss Minimization for Protein Sequence Alignment

• Fan Ding
• Nan Jiang 0012
• Jianzhu Ma
• Jian Peng 0001
• Jinbo Xu
• Yexiang Xue

Protein sequence alignment is a fundamental problem in computational structure biology and popular for protein 3D structural prediction and protein homology detection. Most of the developed programs for detecting protein sequence alignments are based upon the likelihood information of amino acids and are sensitive to alignment noises. We present a novel method PALM for modeling pairwise protein structure alignments, using the area distance to reduce the biological measurement noise. PALM generatively learn the alignment of two protein sequences with probabilistic area distance objective, which can denoise the measurement errors contained in the ground-truth alignments. During learning, we show that the optimization is computationally efficient by estimating the gradients via dynamically sampling alignments. Empirically, we show that PALM can generate sequence alignments with higher precision and recall, as well as smaller area distance than the competing methods especially for long protein sequences and remote homologies. This study implies for learning over large-scale protein sequence alignment problems, one could potentially give PALM a try.

ICML Conference 2021 Conference Paper

XOR-CD: Linearly Convergent Constrained Structure Generation

• Fan Ding
• Jianzhu Ma
• Jinbo Xu
• Yexiang Xue

We propose XOR-Contrastive Divergence learning (XOR-CD), a provable approach for constrained structure generation, which remains difficult for state-of-the-art neural network and constraint reasoning approaches. XOR-CD harnesses XOR-Sampling to generate samples from the model distribution in CD learning and is guaranteed to generate valid structures. In addition, XOR-CD has a linear convergence rate towards the global maximum of the likelihood function within a vanishing constant in learning exponential family models. Constraint satisfaction enabled by XOR-CD also boosts its learning performance. Our real-world experiments on data-driven experimental design, dispatching route generation, and sequence-based protein homology detection demonstrate the superior performance of XOR-CD compared to baseline approaches in generating valid structures as well as capturing the inductive bias in the training set.

ICML Conference 2015 Conference Paper

Learning Scale-Free Networks by Dynamic Node Specific Degree Prior

• Qingming Tang
• Siqi Sun
• Jinbo Xu

Learning network structure underlying data is an important problem in machine learning. This paper presents a novel degree prior to study the inference of scale-free networks, which are widely used to model social and biological networks. In particular, this paper formulates scale-free network inference using Gaussian Graphical model (GGM) regularized by a node degree prior. Our degree prior not only promotes a desirable global degree distribution, but also exploits the estimated degree of an individual node and the relative strength of all the edges of a single node. To fulfill this, this paper proposes a ranking-based method to dynamically estimate the degree of a node, which makes the resultant optimization problem challenging to solve. To deal with this, this paper presents a novel ADMM (alternating direction method of multipliers) procedure. Our experimental results on both synthetic and real data show that our prior not only yields a scale-free network, but also produces many more correctly predicted edges than existing scale-free inducing prior, hub-inducing prior and the l_1 norm.

NeurIPS Conference 2015 Conference Paper

Learning structured densities via infinite dimensional exponential families

• Siqi Sun
• Mladen Kolar
• Jinbo Xu

Learning the structure of a probabilistic graphical models is a well studied problem in the machine learning community due to its importance in many applications. Current approaches are mainly focused on learning the structure under restrictive parametric assumptions, which limits the applicability of these methods. In this paper, we study the problem of estimating the structure of a probabilistic graphical model without assuming a particular parametric model. We consider probabilities that are members of an infinite dimensional exponential family, which is parametrized by a reproducing kernel Hilbert space (RKHS) H and its kernel $k$. One difficulty in learning nonparametric densities is evaluation of the normalizing constant. In order to avoid this issue, our procedure minimizes the penalized score matching objective. We show how to efficiently minimize the proposed objective using existing group lasso solvers. Furthermore, we prove that our procedure recovers the graph structure with high-probability under mild conditions. Simulation studies illustrate ability of our procedure to recover the true graph structure without the knowledge of the data generating process.

UAI Conference 2015 Conference Paper

Structure Learning Constrained by Node-Specific Degree Distribution

• Jianzhu Ma
• Feng Zhao
• Jinbo Xu

We consider the problem of learning the structure of a Markov Random Field (MRF) when a node-specific degree distribution is provided. The problem setting is inspired by protein contact map (i. e. , graph) prediction in which the contact number (i. e. , degree) of an individual residue (i. e. , node) can be predicted without knowing the contact graph. We formulate this problem using maximum pseudo-likelihood plus a node-specific ℓ1 regularization derived from the predicted degree distribution. Intuitively, when a node have 𝑘 predicted edges, we dynamically reduce the regularization coefficients of the 𝑘 most possible edges to promote their occurrence. We then optimize the objective function using ADMM and an Iterative Maximum Cost Bipartite Matching algorithm. Our experimental results show that using degree distribution as a constraint may lead to significant performance gain when the predicted degree has good accuracy. 1.

NeurIPS Conference 2009 Conference Paper

Conditional Neural Fields

• Jian Peng
• Liefeng Bo
• Jinbo Xu

Conditional random fields (CRF) are quite successful on sequence labeling tasks such as natural language processing and biological sequence analysis. CRF models use linear potential functions to represent the relationship between input features and outputs. However, in many real-world applications such as protein structure prediction and handwriting recognition, the relationship between input features and outputs is highly complex and nonlinear, which cannot be accurately modeled by a linear function. To model the nonlinear relationship between input features and outputs we propose Conditional Neural Fields (CNF), a new conditional probabilistic graphical model for sequence labeling. Our CNF model extends CRF by adding one (or possibly several) middle layer between input features and outputs. The middle layer consists of a number of hidden parameterized gates, each acting as a local neural network node or feature extractor to capture the nonlinear relationship between input features and outputs. Therefore, conceptually this CNF model is much more expressive than the linear CRF model. To better control the complexity of the CNF model, we also present a hyperparameter optimization procedure within the evidence framework. Experiments on two widely-used benchmarks indicate that this CNF model performs significantly better than a number of popular methods. In particular, our CNF model is the best among about ten machine learning methods for protein secondary tructure prediction and also among a few of the best methods for handwriting recognition.

TCS Journal 2009 Journal Article

Finding compact structural motifs

• Dongbo Bu
• Ming Li
• Shuai Cheng Li
• Jianbo Qian
• Jinbo Xu

Protein structural motif detection has important applications in structural genomics. Compared with sequence motifs, structural motifs are more sensitive in revealing the evolutionary relationships among proteins. A variety of algorithms have been proposed to attack this problem. However, they are either heuristic without theoretical performance guarantee, or inefficient due to employing exhaustive search strategies. This paper studies a reasonably restricted version of this problem: the compact structural motif problem. We prove that this restricted version is still NP-hard, and we present a polynomial-time approximation scheme to solve it. This is the first approximation algorithm with a guaranteed ratio for the protein structural motif problem. 1 1 A preliminary version of this paper appeared in CPM’2007.

SODA Conference 2008 Conference Paper

Graph algorithms for biological systems analysis

• Bonnie Berger
• Rohit Singh 0001
• Jinbo Xu

TCS Journal 2003 Journal Article

Approximation algorithms for NMR spectral peak assignment

• Zhi-Zhong Chen
• Tao Jiang
• Guohui Lin
• Jianjun Wen
• Dong Xu
• Jinbo Xu
• Ying Xu

We study a constrained bipartite matching problem where the input is a weighted bipartite graph G=(U, V, E), U is a set of vertices following a sequential order, V is another set of vertices partitioned into a collection of disjoint subsets, each following a sequential order, and E is a set of edges between U and V with non-negative weights. The objective is to find a matching in G with the maximum weight that satisfies the given sequential orders on both U and V, i. e. if u i+1 follows u i in U and if v j+1 follows v j in V, then u i is matched with v j if and only if u i+1 is matched with v j+1. The problem has recently been formulated as a crucial step in an algorithmic approach for interpreting NMR spectral data (IEEE Comput. Sci. Eng. 4 (2002) 50–62). The interpretation of NMR spectral data is known as a key problem in protein structure determination via NMR spectroscopy. Unfortunately, the constrained bipartite matching problem is NP-hard (IEEE Comput. Sci. Eng. 4 (2002) 50–62). We first propose a 2-approximation algorithm for the problem, which follows directly from the recent result of Bar-Noy et al. (Proc. 32nd ACM Symp. on Theory of Computing (STOC’00), 2000, pp. 735–744) on interval scheduling. However, our extensive experimental results on real NMR spectral data illustrate that the algorithm performs poorly in terms of recovering target-matching edges. We then propose another approximation algorithm that tries to take advantage of the “density” of the sequential order information in V. Although we are only able to prove an approximation ratio of 3 log 2 D for this algorithm, where D is the length of a longest string in V, the experimental results demonstrate that this new algorithm performs much better on real data, i. e. it is able to recover a large fraction of target-matching edges and the weight of its output matching is often in fact close to the maximum. We also prove that the problem is MAX SNP-hard, even if the input bipartite graph is unweighted. We further present an approximation algorithm for a nontrivial special case that breaks the ratio 2 barrier.