Quantum biologics discovery in healthcare can save many lives

4 April 2024

We're thrilled to announce PEACCEL's pioneering paper in the realm of Quantum Computing for biologics, marking a significant leap forward in pharmaceutical discovery. Our cutting-edge research, conducted in collaboration with the University of Western Australia's esteemed Quantum Physics and Computing team - Shengxin Zhuang, John Tanner, Yusen Wu, under the great leadership of Prof Jingbo Wang, Ass. Prof Du Huynh, and Ass. Prof Weil Liu - demonstrates the untapped potential of Quantum Machine Learning (QML) in the biopharmaceutical industry.

🌐 Quantum Advantages in Computational Biology 🌐

This research underlines the significant, yet largely untapped, advantages of quantum computing in the field of computational biology. By demonstrating that QSVM can exceed the performance of classical machine learning models, we provide empirical evidence and hope for quantum advantages in real-world applications, particularly in developing safer therapeutic solutions.

🔬 Why This Matters for Pharma 🔬

In our study, we applied the quantum support vector machine (QSVM, up to 14 Qubits) to a crucial task: classifying peptides as either hemolytic or non-hemolytic. According to the French national health database, 160 drugs have been shown to induce autoimmune hemolytic anemia for the period 2012-2018, and 3,371 cases of Drug Induce Autoimmune Hemolytic anemia (DI-AIHA) were recorded (https://doi.org/10.1182/blood-2022-157730). 1% wrong class assignment means more human deaths by hemolysis. A 1% gain in accuracy in healthcare can save many lives.

The implications of this work for the pharmaceutical industry are profound. We can harness the quantum leap to expedite the development of novel biologics and transform patient care worldwide.

For more information: https://arxiv.org/abs/2402.03847

PEACCEL
Making the world disease free
Contact: AI-team@peaccel.com
http://www.peaccel.com/

Revolutionizing Biologics Drug Discovery with AI: A Leap Forward with PEACCEL and Google Cloud

8 March 2024

We're thrilled to share an exciting milestone at PEACCEL - our disruptive AI discovery platform for biologics is setting new benchmarks in the pharmaceutical industry. By unlocking previously inaccessible spaces, we are identifying novel, life-changing drugs that were only possible in nature 1% of the time. Imagine the potential when we can screen 1 billion molecules in a single day!

🌐 A Game-Changing Collaboration: PEACCEL & Google Cloud 🌐

Our journey has taken an exhilarating turn as we were handpicked to join the Google Startup Program. This collaboration is not just an endorsement of our innovative approach; it's a catalyst for exponential growth and impact. Here's what this means for the pharmaceutical industry:

Massive Scale Screening: From millions to over a billion molecules per day, pushing the boundaries of drug candidate identification from good to the BEST.
Industrial Scale AI: Our platform's scalability is now supercharged, ready for real-world applications at an industrial level.
Unprecedented Computing Power: Access to a vast array of GPUs, expanding our computing capacity to accelerate drug development, reduce costs, and bring life-saving treatments to market faster.
Robust Infrastructure & Tools: Leveraging Google's state-of-the-art technology to enhance our research capabilities, foster innovation and drive forward the future of drug discovery.

(more…)

Unlocking the Future of Pharma: PEACCEL’s Breakthrough AI Platform for Biologics Wins French DeepTech Label.

29 February 2024

We are thrilled to announce that PEACCEL has been awarded the prestigious French DeepTech label by BPI France, the public investment bank dedicated to innovation. This recognition is a testament to our commitment to pushing the boundaries of biotechnology with our groundbreaking AI discovery platform for Biologics.

(more…)

Epistasis is a crucial issue: Non-Linear responses can hinder drug target identification or optimization of industrial production processes

15 December 2022

Protein engineering in the context of metabolic engineering has a growing impact in industrial biotechnology and synthetic biology. Epistasis is a crucial issue: Non-Linear responses can hinder drug target identification or optimization of industrial production processes.

Discover our Editorial for a special issue of Frontiers in Molecular Biosciences: Machine learning, epistasis, and protein engineering: From sequence-structure-function relationships to regulation of metabolic pathways VOL I.

For more information:

PEACCEL
Making the world disease free
Contact: AI-team@peaccel.com
http://www.peaccel.com/

Maximize your next value creation opportunity for peptides, proteins, enzymes, antibodies & VHHs. Meet Peaccel’s delegates

6 December 2022

Maximize your next value creation opportunity for peptides, proteins, enzymes, antibodies & VHHs. Stand out of the reach of your competitors.

EPI_technology™ (Epistasis Processing Inside): Unique on the market, better & quicker for protein design & optimization

Meet Peaccel’s delegates Jean-Marie Vallet and Jacob Weintraub at the Synthetic Biology-Based Therapies Summit, December 14-15 2022, Boston, US: https://synthetic-biology-therapeutics-summit.com

For more information:

PEACCEL
Making the world disease free
Contact: AI-team@peaccel.com
http://www.peaccel.com/

How the success rate of protein engineering projects is influenced by epistatic effects? A comparison of 15 state-of-the-art approaches

29 November 2022

The recent development of structure prediction deep learning (DL) tools such as Alphafold2 (DeepMind), ESMfold (Meta) or ProteinMPNN (David Baker’s team) has revolutionized this area. Nevertheless, these DL tools are not suitable for predicting how individual amino acid changes alter protein function: they can’t predict epistaticeffects.

After protein folding powered by DeepMind and Meta the next challenge is to accurately predict epistasis ie. the impact of non-linear interactions of mutations within the protein sequence on the function.

Our recent review of Machine Learning (ML) and Deep Learning (DL) strategies examines how epistatic effects influence the success rate of protein engineering projects by comparing 15 state-of-the-art approaches (see Table 4: https://link.springer.com/protocol/10.1007/978-1-0716-2152-3_15/tables/4) and provides a general workflow for non-experts when using such learning strategies.

For more information:

PEACCEL
Making the world disease free
Contact: AI-team@peaccel.com
http://www.peaccel.com/