Portrait of Zhihao Zhan is unavailable

Zhihao Zhan

PhD - Université de Montréal
Supervisor
Jian Tang
Research Topics
Computational Biology
Deep Learning
Generative Models
Medical Machine Learning
Molecular Modeling
Multimodal Learning
Natural Language Processing
Representation Learning
GitHub

Publications

Controllable Generation of Drug-like Molecules with Multi-modal Variational Flow
Fang Sun
Zhihao Zhan
Hongyu Guo
Ming Zhang
Jian Tang
Yizhou Sun
Designing drug molecules that bind effectively to target proteins while maintaining desired pharmacological properties remains a fundamental… (see more) challenge in drug discovery. Current approaches struggle to simultaneously control molecular topology and 3D geometry, often requiring expensive retraining for new design objectives. We propose a multi-modal variational flow framework that addresses these limitations by integrating a 2D topology encoder with a 3D geometry generator. Our architecture encodes molecular graphs into a learned latent distribution via junction tree representations, then employs normalizing flows to autoregressively generate atoms in 3D space conditioned on the protein binding site. This design enables zero-shot controllability: by manipulating the latent prior distribution, we can generate molecules with specific substructures or optimized properties without model retraining. Experiments on the CrossDocked benchmark show that our model achieves 31.1% high-affinity rate, substantially outperforming existing methods, while maintaining superior drug-likeness and structural diversity. Our framework opens new possibilities for on-demand molecular design, allowing medicinal chemists to rapidly explore chemical space with precise control over both structural motifs and physicochemical properties.
2025-10-22
logconference.io/LOG/2025/Conference (poster)
openreview.net
openreview.net
Overcoming Long-Context Limitations of State-Space Models via Context-Dependent Sparse Attention
Zhihao Zhan
Jianan Zhao
Zhaocheng Zhu
Jian Tang
Efficient long-context modeling remains a critical challenge for natural language processing (NLP), as the time complexity of the predominan… (see more)t Transformer architecture scales quadratically with the sequence length. While state-space models (SSMs) offer alternative sub-quadratic solutions, they struggle to capture long-range dependencies effectively. In this work, we focus on analyzing and improving the long-context modeling capabilities of SSMs. We show that the widely used synthetic task, associative recall, which requires a model to recall a value associated with a single key without context, insufficiently represents the complexities of real-world long-context modeling. To address this limitation, we extend the associative recall to a novel synthetic task, \emph{joint recall}, which requires a model to recall the value associated with a key given in a specified context. Theoretically, we prove that SSMs do not have the expressiveness to solve multi-query joint recall in sub-quadratic time complexity. To resolve this issue, we propose a solution based on integrating SSMs with Context-Dependent Sparse Attention (CDSA), which has the expressiveness to solve multi-query joint recall with sub-quadratic computation. To bridge the gap between theoretical analysis and real-world applications, we propose locality-sensitive Hashing Attention with sparse Key Selection (HAX), which instantiates the theoretical solution and is further tailored to natural language domains. Extensive experiments on both synthetic and real-world long-context benchmarks show that HAX consistently outperforms SSM baselines and SSMs integrated with context-independent sparse attention (CISA).
2025-06-11
ICML.cc/2025/Workshop/ES-FoMo-III (published)
doi.org
openreview.net
Cell ontology guided transcriptome foundation model
Xinyu Yuan
Zhihao Zhan
Zuobai Zhang
Manqi Zhou
Jianan Zhao
Boyu Han
Yue Li
Jian Tang
Transcriptome foundation models (TFMs) hold great promises of deciphering the transcriptomic language that dictate diverse cell functions by… (see more) self-supervised learning on large-scale single-cell gene expression data, and ultimately unraveling the complex mechanisms of human diseases. However, current TFMs treat cells as independent samples and ignore the taxonomic relationships between cell types, which are available in cell ontology graphs. We argue that effectively leveraging this ontology information during the TFM pre-training can improve learning biologically meaningful gene co-expression patterns while preserving TFM as a general purpose foundation model for downstream zero-shot and fine-tuning tasks. To this end, we present **s**ingle **c**ell, **Cell-o**ntology guided TFM (scCello). We introduce cell-type coherence loss and ontology alignment loss, which are minimized along with the masked gene expression prediction loss during the pre-training. The novel loss component guide scCello to learn the cell-type-specific representation and the structural relation between cell types from the cell ontology graph, respectively. We pre-trained scCello on 22 million cells from CellxGene database leveraging their cell-type labels mapped to the cell ontology graph from Open Biological and Biomedical Ontology Foundry. Our TFM demonstrates competitive generalization and transferability performance over the existing TFMs on biologically important tasks including identifying novel cell types of unseen cells, prediction of cell-type-specific marker genes, and cancer drug responses.
2024-10-11
NeurIPS.cc/2024/Workshop/FM4Science (poster)
openreview.net
openreview.net
Cell ontology guided transcriptome foundation model
Xinyu Yuan
Zhihao Zhan
Zuobai Zhang
Manqi Zhou
Jianan Zhao
Boyu Han
Yue Li
Jian Tang
Transcriptome foundation models (TFMs) hold great promises of deciphering the transcriptomic language that dictate diverse cell functions by… (see more) self-supervised learning on large-scale single-cell gene expression data, and ultimately unraveling the complex mechanisms of human diseases. However, current TFMs treat cells as independent samples and ignore the taxonomic relationships between cell types, which are available in cell ontology graphs. We argue that effectively leveraging this ontology information during the TFM pre-training can improve learning biologically meaningful gene co-expression patterns while preserving TFM as a general purpose foundation model for downstream zero-shot and fine-tuning tasks. To this end, we present **s**ingle **c**ell, **Cell**-**o**ntology guided TFM (scCello). We introduce cell-type coherence loss and ontology alignment loss, which are minimized along with the masked gene expression prediction loss during the pre-training. The novel loss component guide scCello to learn the cell-type-specific representation and the structural relation between cell types from the cell ontology graph, respectively. We pre-trained scCello on 22 million cells from CellxGene database leveraging their cell-type labels mapped to the cell ontology graph from Open Biological and Biomedical Ontology Foundry. Our TFM demonstrates competitive generalization and transferability performance over the existing TFMs on biologically important tasks including identifying novel cell types of unseen cells, prediction of cell-type-specific marker genes, and cancer drug responses. Source code and model weights are available at https://github.com/DeepGraphLearning/scCello.
2024-09-25
NeurIPS.cc/2024/Conference (spotlight)
openreview.net
openreview.net
GraphVF: Controllable Protein-Specific 3D Molecule Generation with Variational Flow
Fang Sun
Zhihao Zhan
Hongyu Guo
Ming Zhang
Jian Tang
Designing molecules that bind to specific target proteins is a fundamental task in drug discovery. Recent generative models leveraging geome… (see more)trical constraints imposed by proteins and molecules have shown great potential in generating protein-specific 3D molecules. Nevertheless, these existing methods fail to generate 3D molecules with 2D skeletal curtailments, which encode pharmacophoric patterns essential to drug potency. To cope with this challenge, we propose GraphVF, which seamlessly integrates geometrical and skeletal restraints into a variational flow framework, where the former is captured through a flow transformation and the latter is encoded by an amortized factorized Gaussian. We empirically verify that our method achieves state-of-the-art performance on protein-specific 3D molecule generation in terms of binding affinity and some other drug properties. In particular, it represents the first controllable geometry-aware, protein-specific molecule generation method, which enables creating 3D molecules with specified chemical sub-structures or drug properties.
2023-02-01
ICLR.cc/2023/Conference (rejected)
openreview.net
openreview.net