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Zhichen Gong

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3 papers

2 author rows

ICML Conference 2024 Conference Paper

Knowledge-aware Reinforced Language Models for Protein Directed Evolution

Yuhao Wang
Qiang Zhang 0026
Ming Qin
Xiang Zhuang
Xiaotong Li
Zhichen Gong
Zeyuan Wang
Yu Zhao 0009

Directed evolution, a cornerstone of protein optimization, is to harness natural mutational processes to enhance protein functionality. Existing Machine Learning-assisted Directed Evolution (MLDE) methodologies typically rely on data-driven strategies and often overlook the profound domain knowledge in biochemical fields. In this paper, we introduce a novel Knowledge-aware Reinforced Language Model (KnowRLM) for MLDE. An Amino Acid Knowledge Graph (AAKG) is constructed to represent the intricate biochemical relationships among amino acids. We further propose a Protein Language Model (PLM)-based policy network that iteratively samples mutants through preferential random walks on the AAKG using a dynamic sliding window mechanism. The novel mutants are actively sampled to fine-tune a fitness predictor as the reward model, providing feedback to the knowledge-aware policy. Finally, we optimize the whole system in an active learning approach that mimics biological settings in practice. KnowRLM stands out for its ability to utilize contextual amino acid information from knowledge graphs, thus attaining advantages from both statistical patterns of protein sequences and biochemical properties of amino acids. Extensive experiments demonstrate the superior performance of KnowRLM in more efficiently identifying high-fitness mutants compared to existing methods.

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ICLR Conference 2023 Conference Paper

Multi-level Protein Structure Pre-training via Prompt Learning

Zeyuan Wang
Qiang Zhang 0026
Shuangwei Hu
Haoran Yu
Xurui Jin
Zhichen Gong
Huajun Chen

A protein can focus on different structure levels to implement its functions. Each structure has its own merit and driving forces in describing some specific characteristics, and they cannot replace each other. Most existing function prediction methods take the tertiary structure as input, unintentionally ignoring the other levels of protein structures. Considering protein sequences can determine multi-level structures, in this paper, we aim to realize the comprehensive potential of protein sequences for function prediction. Specifically, we propose a new prompt-guided multi-task pre-training and fine-tuning framework, and the resulting protein model is called PromptProtein. Through the prompt-guided multi-task pre-training, we learn multiple prompt signals to steer the model to focus on different structure levels. We also design a prompt fine-tuning module to provide downstream tasks the on-demand flexibility of utilizing respective levels of structure information. Extensive experiments on function prediction and protein engineering show that PromptProtein outperforms state-of-the-art methods by large margins.

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IJCAI Conference 2019 Conference Paper

A Regularized Opponent Model with Maximum Entropy Objective

Zheng Tian
Ying Wen
Zhichen Gong
Faiz Punakkath
Shihao Zou
Jun Wang

In a single-agent setting, reinforcement learning (RL) tasks can be cast into an inference problem by introducing a binary random variable o, which stands for the "optimality". In this paper, we redefine the binary random variable o in multi-agent setting and formalize multi-agent reinforcement learning (MARL) as probabilistic inference. We derive a variational lower bound of the likelihood of achieving the optimality and name it as Regularized Opponent Model with Maximum Entropy Objective (ROMMEO). From ROMMEO, we present a novel perspective on opponent modeling and show how it can improve the performance of training agents theoretically and empirically in cooperative games. To optimize ROMMEO, we first introduce a tabular Q-iteration method ROMMEO-Q with proof of convergence. We extend the exact algorithm to complex environments by proposing an approximate version, ROMMEO-AC. We evaluate these two algorithms on the challenging iterated matrix game and differential game respectively and show that they can outperform strong MARL baselines.

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