Machine Learning using neural networks has received prominent attention recently because of its success in solving a wide variety of computational tasks, in particular in the field of computer vision. However, several works have drawn attention to potential security risks involved with the training and implementation of such networks. In this work, we introduce DeepBaR, a novel approach that implants backdoors on neural networks by faulting their behavior at training, especially during fine-tuning. Our technique aims to generate adversarial samples by optimizing a custom loss function that mimics the implanted backdoors while adding an almost non-visible trigger in the image. We attack three popular convolutional neural network architectures and show that DeepBaR attacks have a success rate of up to 98.30\%. Furthermore, DeepBaR does not significantly affect the accuracy of the attacked networks after deployment when non-malicious inputs are given. Remarkably, DeepBaR allows attackers to choose an input that looks similar to a given class, from a human perspective, but that will be classified as belonging to an arbitrary target class.

Machine learning can quickly and precisely evaluate binding free energy used in drug discovery, according to a March 15 study published in The Journal of Physical Chemistry Letters. The new machine learning tool, known as DeepBAR, was discovered by Xinqiang Ding, PhD, and Bin Zhang, PhD, researchers from the Massachusetts Institute of Technology in Cambridge. Drugs are only effective if they stick to their target proteins in the body, which can slow down drug discovery. Existing techniques struggle to balance efficiency and accuracy, researchers said. DeepBAR can accelerate the process because it is much quicker than other methods currently available.

Artificial intelligence and machine learning techniques are already proving effective in pharmaceutical procedures. Drug discovery is one of the crucial procedures to find new candidate medications in the field of medicine, biotechnology and pharmacology. According to the U.S. FDA, there are five steps for the development of a new drug. These include discovery and development, preclinical research, clinical research, FDA review, and FDA post-market safety monitoring. Since drug discovery requires huge amounts of data and research, many pharmaceutical companies are embracing AI and machine learning to accelerate the pace of drug discovery.

Drugs can only work if they stick to their target proteins in the body. Assessing that stickiness is a key hurdle in the drug discovery and screening process. The new technique, dubbed DeepBAR, quickly calculates the binding affinities between drug candidates and their targets. The approach yields precise calculations in a fraction of the time compared to previous state-of-the-art methods. The researchers say DeepBAR could one day quicken the pace of drug discovery and protein engineering.

Drugs can only work if they stick to their target proteins in the body. Assessing that stickiness is a key hurdle in the drug discovery and screening process. The new technique, dubbed DeepBAR, quickly calculates the binding affinities between drug candidates and their targets. The approach yields precise calculations in a fraction of the time compared to previous state-of-the-art methods. The researchers say DeepBAR could one day quicken the pace of drug discovery and protein engineering.