Collaborating Authors


Antimicrobial resistance with Artificial Intelligence


Minh-Hoang Tran,1 Ngoc Quy Nguyen,2 Hong Tham Pham1,3 1Department of Pharmacy, Nhan Dan Gia Dinh Hospital, Ho Chi Minh City, Vietnam; 2Institute of Environmental Technology and Sustainable Development, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam; 3Department of Pharmacy, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam Correspondence: Hong Tham Pham, Department of Pharmacy, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam, Tel 84 919 559 085, Email [email protected] Abstract: Recent years have witnessed the rise of artificial intelligence (AI) in antimicrobial resistance (AMR) management, implying a positive signal in the fight against antibiotic-resistant microbes. The impact of AI starts with data collection and preparation for deploying AI-driven systems, which can lay the foundation for some effective infection control strategies. Primary applications of AI include identifying potential antimicrobial molecules, rapidly testing antimicrobial susceptibility, and optimizing antibiotic combinations. Aside from their outstanding effectiveness, these applications also express high potential in narrowing the burden gap of AMR among different settings around the world. Despite these benefits, the interpretability of AI-based systems or models remains vague.

What's next for AlphaFold and the AI protein-folding revolution


For more than a decade, molecular biologist Martin Beck and his colleagues have been trying to piece together one of the world's hardest jigsaw puzzles: a detailed model of the largest molecular machine in human cells. This behemoth, called the nuclear pore complex, controls the flow of molecules in and out of the nucleus of the cell, where the genome sits. Hundreds of these complexes exist in every cell. Each is made up of more than 1,000 proteins that together form rings around a hole through the nuclear membrane. These 1,000 puzzle pieces are drawn from more than 30 protein building blocks that interlace in myriad ways. Making the puzzle even harder, the experimentally determined 3D shapes of these building blocks are a potpourri of structures gathered from many species, so don't always mesh together well. And the picture on the puzzle's box -- a low-resolution 3D view of the nuclear pore complex -- lacks sufficient detail to know how many of the pieces precisely fit together. In 2016, a team led by Beck, who is based at the Max Planck Institute of Biophysics (MPIBP) in Frankfurt, Germany, reported a model1 that covered about 30% of the nuclear pore complex and around half of the 30 building blocks, called Nup proteins.

From Israeli lab: First AI-designed antibody enters clinical trials


Aulos Biosciences is now recruiting cancer patients in Australian medical centers for a trial of the world's first antibody drug designed by a computer. The computationally designed antibody, known as AU-007, was planned by the artificial intelligence platform of Israeli biotech company Biolojic Design from Rehovot, in a way that would target a protein in the human body known as interleukin-2 (IL-2). The goal is for the IL-2 pathway to activate the body's immune system and attack the tumors. The clinical trial will be conducted on patients with final stage solid tumors and will last about a year – but the company hopes to present interim results during 2022. The trial has raised great hopes because if it is successful, it will pave the way for the development of a new type of drug using computational biology and "big data."

Machine learning identifies antibiotic resistant bacteria that can spread between animals, humans and environment


Experts from the University of Nottingham have developed new software which combines DNA sequencing and machine learning to help them find where, and to what extent, antibiotic resistant bacteria is being transmitted between humans, animals and the environment. The study, which is published in PLOS Computational Biology, was led by Dr. Tania Dottorini from the School of Veterinary Medicine and Science at the University. Anthropogenic environments (spaces created by humans), such as areas of intensive livestock farming, are seen as ideal breeding grounds for antimicrobial-resistant bacteria and antimicrobial resistant genes, which are capable of infecting humans and carrying resistance to drugs used in human medicine. This can have huge implications for how certain illnesses and infections can be treated effectively. In this new study, a team of experts looked at a large scale commercial poultry farm in China, and collected 154 samples from animals, carcasses, workers and their households and environments.

How artificial intelligence is revolutionising drug design


Imagine you wanted to design a drug for a new disease, 'Disease X', about which little is known. Imagine then that you have a machine that could use all the available data in the world about Disease X to identify a potential mechanism of disease and use this to predict which molecules within this mechanism could make suitable targets for drugs against the disease. Then, a machine would virtually design a drug targeting these optimal molecules, building it bit by bit and continuously checking with the target's structure to ensure activity at the desired binding site. Once the drug was "built", it could then be synthesised and, following various rounds of in vitro, in vivo, and clinical testing to validate its efficacy, the drug could be used in clinical practice. Although a machine like this does not yet exist, advocates of artificial intelligence (AI) propose that AI has the potential to revolutionise drug design, turning this imaginary scenario -- at least in part -- into a reality.

Increasing access and equity in healthcare through AI - MedCity News


One of the racial disparities long seen in healthcare lies in minority races returning less frequently for follow-up appointments. AI and remote patient monitoring can be powerful tools to give providers insight into the day-to-day factors impacting a patient's health. Advanced algorithms can process large data sets including clinical and socioeconomic information to give a holistic view of the individual, and AI has the ability to suggest what approaches will work most successfully to not only get patients activated, but keep them engaged. With the ability to collect data from patient devices and more, AI and patient monitoring provide additional data sources to refine the patient experience, including prime times for engagement – such as attending critical follow-up appointments.

AI-based drug discovery with Atomwise and WEKA Data Platform - HPCwire


The Covid-19 pandemic has profoundly changed the world. The remote workplace has become the norm. We have started looking at personal health differently – the way we work, live, play and do business. AI's use for drug discovery has accelerated post-Covid-19 era. Today, drug discovery is an expensive proposition, with a $2.6 billion cost over 10 years and just a 12% success rate.

Fairness in Influence Maximization through Randomization

Journal of Artificial Intelligence Research

The influence maximization paradigm has been used by researchers in various fields in order to study how information spreads in social networks. While previously the attention was mostly on efficiency, more recently fairness issues have been taken into account in this scope. In the present paper, we propose to use randomization as a mean for achieving fairness. While this general idea is not new, it has not been applied in this area. Similar to previous works like Fish et al. (WWW ’19) and Tsang et al. (IJCAI ’19), we study the maximin criterion for (group) fairness. In contrast to their work however, we model the problem in such a way that, when choosing the seed sets, probabilistic strategies are possible rather than only deterministic ones. We introduce two different variants of this probabilistic problem, one that entails probabilistic strategies over nodes (node-based problem) and a second one that entails probabilistic strategies over sets of nodes (set-based problem). After analyzing the relation between the two probabilistic problems, we show that, while the original deterministic maximin problem was inapproximable, both probabilistic variants permit approximation algorithms that achieve a constant multiplicative factor of 1 − 1/e minus an additive arbitrarily small error that is due to the simulation of the information spread. For the node-based problem, the approximation is achieved by observing that a polynomial-sized linear program approximates the problem well. For the set-based problem, we show that a multiplicative-weight routine can yield the approximation result. For an experimental study, we provide implementations of multiplicative-weight routines for both the set-based and the node-based problems and compare the achieved fairness values to existing methods. Maybe non-surprisingly, we show that the ex-ante values, i.e., minimum expected value of an individual (or group) to obtain the information, of the computed probabilistic strategies are significantly larger than the (ex-post) fairness values of previous methods. This indicates that studying fairness via randomization is a worthwhile path to follow. Interestingly and maybe more surprisingly, we observe that even the ex-post fairness values, i.e., fairness values of sets sampled according to the probabilistic strategies computed by our routines, dominate over the fairness achieved by previous methods on many of the instances tested.

When scientific information is dangerous


One big hope about AI as machine learning improves is that we'll be able to use it for drug discovery -- harnessing the pattern-matching power of algorithms to identify promising drug candidates much faster and more cheaply than human scientists could alone. But we may want to tread cautiously: Any system that is powerful and accurate enough to identify drugs that are safe for humans is inherently a system that will also be good at identifying drugs that are incredibly dangerous for humans. They took a machine learning model they'd trained to find non-toxic drugs, and flipped its directive so it would instead try to find toxic compounds. In less than six hours, the system identified tens of thousands of dangerous compounds, including some very similar to VX nerve gas. Their paper hits on three interests of mine, all of which are essential to keep in mind while reading alarming news like this.

These 2021 Biotech Breakthroughs Will Shape the Future of Health and Medicine


With 2021 behind us, we're going down memory lane to highlight biotech innovations that shaped the year--with impact that will likely reverberate for many years to come. Covid-19 dominated the news, but science didn't stand still. CRISPR spun off variations with breathtaking speed, expanding into a hefty toolbox packed with powerhouse gene editors far more efficient, reliable, and safer than their predecessors. CRISPRoff, for example, hijacks epigenetic processes to reversibly turn genes on and off--all without actually snipping or damaging the gene itself. Prime editing, the nip-tuck of DNA editing that only snips--rather than fully cutting--DNA received an upgrade to precisely edit up to 10,000 DNA letters in a variety of cells.