multi-level parallelism
high-performance computing architecture
manual intervention support
interactive charts
integration of multi-platforms, tools, and databases
dynamic resource allocation & optimized load balancing
tiered permissions & priority control
end-to-end encryption & access control
independent storage & access
distributed computing & automatic fault tolerance
log recording & tracking
API integration & custom algorithm development
microsecond-level simulation
MAE as low as 1.0 kcal/mol
differentiated predictive model selection
continuous AI model optimization & updates
support for various AI models via open-source frameworks

On November 29, MicroQuin published an advanced study in the Nature Portfolio titled “TMBIM6/BI-1 is an intracellular environmental regulator that induces paraptosis in cancer via ROS and Calcium-activated ERAD II pathways”. The article unveils the how the transmembrane B cell lymphoma 2-associated X protein (BAX) inhibitor motif-containing TMBIM6 can treat a variety of cancers. TMBIM6 as an IntraCellular Environmental (ICE) regulator induces paraptosis via noncanonical routes — ERAD II mechanisms. TMBIM6 agonism can induces rapid cancer cell death with no toxicity, representing an attractive therapeutic target across various diseases and injuries, including cancer. Dr. Yue Qian, Executive Director of the Computational Chemistry and Artificial Intelligence for Drug Discovery (AIDD/CADD) Platform at Viva Biotech, Hai Liu, Project Manager of the AIDD/CADD platform, and Dr. David Taddei, Vice President of Medicinal Chemistry provided computational insights to the function-related protein dynamics under different conditions. The in silico modeling made significant contributions to understanding the mechanism of action and experimental design.

(Source: Nature Oncogene website)
TMBIM 6 has been heavily researched for its cytoprotective functions. It's functional diversity includes modulating cell survival, stress, metabolism, etc. Clinical research shows TMBIM6 plays a key role in many of the world's top diseases/injuries i.e., Alzheimer's, Parkinson's, and cancer, where TMBIM6 expression impacts patient survival, chemoresistance, cancer progression, and metastasis. The study shows TMBIM6 is activated by, and undergoes, different conformational changes that dictate its function following a significant change in the cell’s ICE. In cancer cells TMBIM6 agonism results in rapid paraptotic induction irrespective of the cancer type, sub-type, genotype or phenotype. Furthermore, the level of TMBIM6 expression in cancer did not dictate the level of paraptotic induction; however, it did dictate the rate at which paraptosis occurred. Instead, TMBIM6 agonism in cancer upregulates cytosolic Ca2+ and reactive oxygen species (ROS), activates lysosome biogenesis, and induces paraptosis via ERAD II mechanisms. In xenograft models, TMBIM6 agonism induces rapid cancer cell death with no toxicity, even at high doses of TMBIM6 agonist (>450 mg/kg). In summary, this study shows TMBIM6's functional diversity is only activated by severe ICE change in diseased/injured cells, highlighting its transformative potential as a therapeutic target across various diseases and injuries, including cancer.
TMBIM6's functional diversity is key in the pathogenesis of many diseases including neurodegenerative disease, ischemic-reperfusion injury, and cancer, etc. The complexity and diversity of TMBIM6's functions pose considerable challenges for researchers to understand its structure and function. TMBIM6 is capable of detecting changes in the cell's intracellular environment (ICE) and reacts with a conformational change, and environmental stimuli (pH, Ca2+, and ROS) also impact on TMBIM6 conformation. The deep understanding of TMBIM6 dynamic behavior is crucial for developing TMBIM6-based therapeutic strategy.
In order to investigate the TMBIM6's capabilities in detecting unique ICE change that induce conformational change to activate specific TMBIM6 functions, Viva Biotech's AIDD/CADD team utilized structural modeling, optimization and constant pH molecular dynamic simulation (CpHMD) to explore TMBIM6's conformational change. Despite that the TMBIM6 structure has not been experimentally determined, the team is able to identify significant changes in the transmembrane region and alpha-helical formation of TMBIM6 under different pH conditions. The CpHMD results fully align with the circular dichroism data (CD) in terms of the secondary structure propensity. This data further supports the idea that TMBIM6 can detect ICE change, which induces structural conformational change. This approach provided insights into how molecular structural changes influence biological functions. By accurately simulating the protein's behavior in dynamic environments and analyzing the roles of key residues, the team established a solid theoretical and structural foundation for TMBIM6-targeted drug design. These findings demonstrate our AIDD/CADD team's advanced capabilities in exploring novel targets and mechanisms in drug discovery.

(Source: Oncogene)
At Viva Biotech, Dr. Yue Qian has established a team of talents that represents the fast-evolving scientific fronts and diverse demographic backgrounds. This team represents a comprehensive AI-driven drug discovery platform that integrates cutting-edge computational methods with real-world experimental observations. Techniques include virtual screening, de novo design, molecular dynamics, free energy perturbation, and a series of deep-learning based algorithms, covering the drug discovery process from target identification to lead optimization and to preclinical research. The team puts together extensive experience in developing a range of drug modalities such as small molecules, antibodies, peptides, and RNA-targeting small molecules. We collaborate seamlessly between the modeling team and the experimental validations, which significantly reduces both the time and cost associated with drug discovery while offering forward-thinking solutions to address critical challenges in pharmaceutical research. With the rapid technological advancement, Viva Biotech harnesses the synergy of AI and experimental technologies to unlock new possibilities in drug discovery and drive the biopharmaceutical industry toward greater precision and efficiency.
For further details on the paper, please refer to:
Robinson, K.S., Sennhenn, P., Yuan, D.S. et al. TMBIM6/BI-1 is an intracellular environmental regulator that induces paraptosis in cancer via ROS and Calcium-activated ERAD II pathways. Oncogene (2024). https://doi.org/10.1038/s41388-024-03222-x
For more information about Viva Biotech's AIDD/CADD platform, please contact our team at info@vivabiotech.com.




Recently, AIxplorerBio, a Viva Biotech portfolio company, published an article titled "Adaptive lambda schemes for efficient relative binding free energy calculation" in the Journal of Computational Chemistry. Dr. Yue Qian, Executive Director of Computational Chemistry at Viva Biotech, is the corresponding author of this paper. This is a highly productive collaboration between AIxplorerBio and Dr. Qian.

The research represents a significant enhancement to the FEP platform, introducing adaptive lambda schemes for the effective calculation of relative binding free energy. The main focus of the research includes:
The relative free energy perturbation (RFEP) calculation is one of the most theoretically sound computational chemistry approaches for the binding affinity prediction. However, its application is often hindered by the complexity of the calculation choices and the requirement of expertise in the field. Improper lambda scheme of RFEP may result in deviations from an accurate description of the perturbation process and is prone to erroneous affinity predictions. To address such challenges, an automated adaptive lambda method is proposed where the adaptive lambda schemes are obtained through a split-and-merge algorithm based on the pilot runs. The newly established workflow along with a series of improvements to the perturbation settings increases the consistency of the RFEP calculation results. Comparing the pilot and adaptive lambda schemes, the latter demonstrated improvements in convergence and reproducibility and lowered the mean unsigned error and the root-mean-square error. Overall, the adaptive lambda method is a reliable and robust choice to predict small molecule relative binding free energy and can be capitalized to benefit routine RFEP calculations for drug discovery projects.

Source: Adaptive Lambda Schemes for Efficient Relative Binding Free Energy Calculation
The research findings have been effectively integrated into AIxplorerBio's small molecule dual-target drug design platform, AIxDDDTM. This platform encompasses three technical modules: target combination assessment, molecular design, and optimization. It aids in the rapid theoretical validation of the synergistic effects of target combinations and the design and optimization of small molecule drugs with high activity and specificity for two targets simultaneously.
The target combination assessment module of AIxDDDTM. leverages the latest advancements in natural language processing (NLP) and large language model (LLM) to analyze relevant multi-omics data. It scores and ranks potential synergistic effects (additive efficacy, antagonistic toxicity) of target combinations in specific disease areas. The molecular design module, inspired by the thought processes of medicinal chemistry experts, utilizes AI technology to generate and continuously refine molecules based on pharmacophore-target interactions, employing reinforcement learning. AIxplorerBio's proprietary free energy calculation method, including the results from this article, plays a crucial role in compound optimization, assisting in the GO/NO GO decision-making process prior to the synthesis of newly designed compounds.
As an expert in the CADD field, Dr. Yue Qian possesses extensive knowledge and practical experience in computational chemistry. As the corresponding author of this paper, she contributed the research ideas, provided professional guidance, and led the writing of the research article.
At Viva Biotech, she leads a professional team that has independently developed an FEP platform from scratch, utilizing the company's high-performance computing system. This platform integrates a user-friendly interface, automated analysis, and golden-standard precision. As this platform is internally developed at Viva Biotech, our research scientists have a comprehensive understanding of the methods and can fine-tune the parameters to optimize FEP calculation conditions with first-hand experiences. Viva Biotech's proprietary free energy perturbation method shows a small mean unsigned error (MUE) of 1 kcal/mol, indicating a high level of accuracy. With the support of the high-performance computing clusters, FEP calculations have now become a routine practice for most of the drug discovery services provided by Viva Biotech.
For further details on the paper, please refer to: J. Zeng, Y. Qian, J. Comput. Chem. 2023, 1. https://doi.org/10.1002/jcc.27287
About AIxplorerBio
AIxplorerBio is an AI-powered drug R&D biotech company. We focus on the discovery and development of new medicines for immunological and neurodegenerative diseases. We are also committed to creating a new AI-powered drug R&D paradigm to enable innovation, efficiency and precision in searching for better medicines.
AIxplorerBio is established by a team of highly experienced cross-discipline experts and sponsored by the new B/IT (biotechnology and information technology) powerhouse, BioMap, and the leading drug discovery CRO platform, Viva Biotech. In addition to the strategic cooperation with BioMap and Viva, AIxplorerBio also actively seeks collaboration opportunities with AI-oriented technology companies as well as drug R&D companies to explore more efficient ways for new drug R&D.