BioMap was co-founded by Baidu's Founder/CEO Robin Li and the former CEO of Baidu Ventures, Wei Liu. We are committed to bringing first-in-class medicine for unmet medical needs in the areas of immune-oncology, autoimmune diseases and aging-related diseases.

Leadership

Robin LI

Founder and Chairman

Wei LIU

Co-founder and CEO

Zhaoshi JIANG

Vice President of Target Discovery

Le SONG

Chief AI Scientist

Xinghua RUAN

Vice President of Engineering Technology

Shuoran LI

Vice President of Innovation

Da ZHOU

Vice President of Ecosystem

Mingzhen ZHANG

General Counsel & Vice President of Operation

Xin GAO

BioComputing Advisor to CEO & Principal Scientist in Protein AI

Cheng-Chi CHAO

Head of Immunology Research

Scientific Advisors

Robert Gentleman

Bioinformatics Scientific Advisor

Yaqin ZHANG

AI Scientific Advisor

Liang LI

Metabolomics Scientific Advisor

Zemin ZHANG

Single Cell Multi-omics Scientific Advisor

Jian ZHANG

AI Drug Design Scientific Advisor

Jianmin WU

Oncology Multi-Omics and Bioinformatics Scientific Advisor

Jing HUANG

MD Simulation and Rational Drug Design Scientific Advisor

Daqiang XU

Innovative Drugs Scientific Advisor

Our Team

We are a team of world-renowned scientists who share an extensive expertise in disease biology, bioinformatics, machine learning/deep learning, and antibody engineering.

Team Member Historical Research

Robin Li

Whole-genome sequencing of 508 patients identifies key molecular features associated with poor prognosis in esophageal squamous cell carcinoma

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Whole-genome sequencing of 508 patients identifies key molecular features associated with poor prognosis in esophageal squamous cell carcinoma

The study reports the largest map of esophageal squamous cell carcinoma (ESCC) genomic analysis. By analyzing 508 ESCC genomes, the study identified 5 new significant mutated genes and revealed 3 major subtypes, defining clusters of mutation features associated with metastasis and patient prognosis. This study was supported by the National Key Research and Development Program of China, CAMS Medical Innovation Fund, National Natural Science Foundation of China, Shenzhen “Three Projects in Healthcare”, and Guangdong Basic and Applied Basic Research Fund. 【Cui, Y., Chen, H., Xi, R. et al. Cell Res. 2020 May; 30, 902-913.】

Dr. Zhaoshi Jiang

The effects of hepatitis B virus integration into the genomes of hepatocellular carcinoma patients

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The effects of hepatitis B virus integration into the genomes of hepatocellular carcinoma patients

Dr. Jiang led the pioneer whole-genome sequencing study in HBV+ HCC patients. The stud systematically explored the HBV integration in the human genome and revealed various genomics consequences of viral integration and their relevance in hepatocellular carcinoma development. The study reported integrated HBV contributed significantly to viral protein production (e.g., HBsAg). This novel finding revised the commonly accepted clinical endpoint of the HBV cure. This study was selected as the cover story of Genome Research in April 2012. 【Jiang et al, Genome Res. 2012 Apr;22(4):593-601】

Dr. Zhaoshi Jiang

Alternative splicing: aberrant splicing promotes colon tumour growth

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Alternative splicing: aberrant splicing promotes colon tumour growth

In this study, Dr. Jiang and colleagues applied an integrated multi-omics and functional genomics approach to identify novel therapeutic targets in colorectal cancer patients. Dr. Jiang applied a novel method to detect intron-retention, which services as evidence of aberrant splicing in the cancer genome. They identified that PRPF6, a critical component of the spliceosome complex, was commonly amplified in colorectal cancer patients. They then functionally validated PRPF6 as an oncogene based on evidence from cell line and animal models; and concluded that PRPF6 and its downstream target ZAK gene could potentially serve as a therapeutic target for colon cancer patients. This seminal study of aberrant splicing in colon cancer was reported by Nature Reviews Cancer as Research Highlights in 2014. 【Genes Dev. 2014 May 15;28(10):1068-84;Nature Reviews Cancer. 2014 (14):382–383 】

Dr. Le Song

Molecule optimization by explainable evolution

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Molecule optimization by explainable evolution

Optimizing molecules for desired properties is a fundamental yet challenging task in chemistry, material science, and drug discovery. This paper develops a novel algorithm for optimizing molecular properties via an ExpectationMaximization (EM) like explainable evolutionary process. The algorithm is designed to mimic human experts in the process of searching for desirable molecules and alternate between two stages: the first stage on explainable local search which identifies rationales, i.e., critical subgraph patterns accounting for desired molecular properties, and the second stage on molecule completion which explores the larger space of molecules containing good rationales. We test our approach against various baselines on a real-world multi-property optimization task where each method is given the same number of queries to the property oracle. We show that our evolution-by-explanation algorithm is 79% better than the best baseline in terms of a generic metric combining aspects such as success rate, novelty, and diversity. Human expert evaluation on optimized molecules shows that 60% of top molecules obtained from our methods are deemed successful.

https://openreview.net/pdf?id=jHefDGsorp5

Dr. Le Song

Retro*: Learning retrosynthetic planning with neural guided A* search

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Dr. Le Song & Dr. Xin Gao

RNA secondary structure prediction by learning unrolled algorithms

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RNA secondary structure prediction by learning unrolled algorithms

In this paper, we propose an end-to-end deep learning model, called E2Efold, for RNA secondary structure prediction which can effectively take into account the inherent constraints in the problem. The key idea of E2Efold is to directly predict the RNA base-pairing matrix, and use an unrolled algorithm for constrained programming as the template for deep architectures to enforce constraints. With comprehensive experiments on benchmark datasets, we demonstrate the superior performance of E2Efold: it predicts significantly better structures compared to previous SOTA (especially for pseudoknotted structures), while being as efficient as the fastest algorithms in terms of inference time.

https://openreview.net/pdf?id=S1eALyrYDH

Dr. Le Song & Dr. Xin Gao

Sequence2Vec: a novel embedding approach for modeling transcription factor binding affinity landscape

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Sequence2Vec: a novel embedding approach for modeling transcription factor binding affinity landscape

While recent advances in biotechnology have brought the opportunity for building binding affinity prediction methods, the accurate characterization of transcription factor (T.F.)-DNA binding affinity landscape still remains a challenging problem. We proposed a novel sequence embedding approach for modeling the transcription factor binding affinity landscape. Our method represents DNA binding sequences as a hidden Markov model (HMM) which captures both position specific information and long-range dependency in the sequence. Our method is a novel combination of the strength of probabilistic graphical models, feature space embedding and deep learning. We conducted comprehensive experiments on over 90 large-scale TF-DNA data sets which were measured by different high-throughput experimental technologies.【Dai et al, Bioinformatics. 2017 33(22): 3575-3583】

Dr. Xin Gao

An integrated structure- and system-based framework to identify new targets of metabolites and known drugs

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An integrated structure- and system-based framework to identify new targets of metabolites and known drugs

The inherent promiscuity of small molecules towards protein targets impedes our understanding of healthy versus diseased metabolism. This promiscuity also poses a challenge for the pharmaceutical industry as identifying all protein targets is important to assess (side) effects and repositioning opportunities for a drug. We proposed a novel integrated structure- and system-based approach of drug-target prediction (iDTP) to enable the large-scale discovery of new targets for small molecules, such as pharmaceutical drugs, co-factors and metabolites (collectively called ‘drugs’). For a given drug, our method uses sequence order–independent structure alignment, hierarchical clustering and probabilistic sequence similarity to construct a probabilistic pocket ensemble (PPE) that captures promiscuous structural features of different binding sites on known targets. A drug’s PPE is combined with an approximation of its delivery profile to reduce false positives. In our cross-validation study, we use iDTP to predict the known targets of 11 drugs, with 63% sensitivity and 81% specificity. We then predicted novel targets for these drugs—two that are of high pharmacological interest, the peroxisome proliferator-activated receptor gamma and the oncogene B-cell lymphoma 2, were successfully validated through in vitro binding experiments. Our method is broadly applicable for the prediction of protein-small molecule interactions with several novel applications to biological research and drug development.【Naveed et al, Bioinformatics. 2015 31(24): 3922-3929】

Dr. Cheng-Chi Chao

A single-cell approach to engineer CD8+ T cells targeting cytomegalovirus

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A single-cell approach to engineer CD8+ T cells targeting cytomegalovirus

Dr. Chao’s group presented a single-cell approach for the generation of TCR-engineered CD8+ T cells. Correlative analysis of the single-cell transcriptome and TCR sequencing data provides a useful strategy to identify functional TCR pairs against immunogenic epitopes. The innovated approach can be widely applied, and significantly enhance the developmental progress of immune cell therapies for the treatment of cancer malignancy and viral-associated diseases such as the ongoing pandemic caused by SARS-CoV-2.【 Cell Mol Immunol. 2021 May;18(5):1326-1328.】

Dr. Cheng-Chi Chao

Anti-IL-17A therapy protects against bone erosion in experimental models of rheumatoid arthritis

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Anti-IL-17A therapy protects against bone erosion in experimental models of rheumatoid arthritis

Dr. Chao led the research in studying the pathogenic role of IL-17 in autoimmune diseases. The study first identified the bone-preserving property of anti-IL-17 correlated with decreased RANKL message in severely inflamed joints. The data identify IL-17A as a key factor in inflammation-mediated bone destruction and support anti-IL-17A therapy for the novel treatment of inflammatory autoimmune diseases such as rheumatoid arthritis, psoriasis and multiple sclerosis etc. 【Autoimmunity. 2011 May;44(3):243-52.】

Dr. Liguang Yang

A Homeostatic Arid1a-Dependent Permissive Chromatin State Licenses Hepatocyte Responsiveness to Liver-Injury-Associated YAP Signaling

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A Homeostatic Arid1a-Dependent Permissive Chromatin State Licenses Hepatocyte Responsiveness to Liver-Injury-Associated YAP Signaling

By constructing mice deficient in Arid1a knockout in liver cells, Yang and his collaborators found that Arid1a deletion inhibits liver damage repair. The ATAC-seq technology was used to study the open regions of chromatin, and it was found that when the liver was in a normal state, Arid1a gave hepatocytes a pre-open state of chromatin reprogramming genes. Combining ChIP-seq and RNA-seq, it is recognized that the transcription factor Yap responds to the Arid1a-mediated hepatocyte reprogramming process. Yap binds to hepatocyte reprogramming genes and transcriptionally activates the expression of hepatocyte reprogramming genes, making hepatocytes respond to Hippo The /Yap signaling pathway reveals the molecular basis of hepatocyte dedifferentiation plasticity.【Cell Stem Cell. 2019 June; 25, 54-68】

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