Quantum computing has rapidly advanced in recent years due to substantial development in both hardware and algorithms. These advances are carrying quantum computers closer to their impending commercial utility. Drug discovery is a promising area of application that will find a number of uses for these new machines. As a prominent example, quantum simulation will enable faster and more accurate characterizations of molecular systems than existing quantum chemistry methods. Furthermore, algorithmic developments in quantum machine learning offer interesting alternatives to classical machine learning techniques, which may also be useful for the biochemical efforts involved in early phases of drug discovery. Meanwhile, quantum hardware is scaling up rapidly into a regime where an exact simulation is difficult even using the world’s largest supercomputers. We review how these recent advances can shift the paradigm with which one thinks about drug discovery, focusing on both the promises and caveats associated with each development. In particular, we highlight how hybrid quantum-classical approaches to quantum simulation and quantum machine learning could yield substantial progress using noisy-intermediate scale quantum devices, whereas fault-tolerant, error-corrected quantum computers are still in their development phase.

Seeking beneficial drug–drug combinations (DDCs) from real-world evidence is an emerging topic in phenotypic drug discovery. With sophisticated algorithms, the numbers of DDC hypotheses generated often reach to tens of thousands. However, due to limited resources, only a few, top-ranking hypotheses are selected for experimental validations. Often, researchers start from the topmost DDC pairs and work their way down until they find a pair with successful validation. While this is an established way of performing validations, there still exists room to improve. Here, we present a systematic approach to perform a secondary analysis on the DDCs to streamline the validation procedure. Specifically, we propose a method in which we search for additional patterns in terms of chemical classes and biological target interactions of the drug pair. Using 78,345 DDC hypotheses generated from the FDA Adverse Event Reporting System (FAERS) data in our prior study, we demonstrate how the proposed analysis can reveal additional biochemical and mechanical insights of drug interactions that can streamline experimental validation.

The IBM POWER9 processor is the latest Reduced Instruction Set Computer microprocessor from IBM. POWER9 employs a new modular core microarchitecture to counter the technology trend of decreasing frequency and increasing power density from generation to generation. The new POWER9 design enables a family of processors optimized for a broad range of server applications. The new microarchitecture is closely coupled with a rich set of new instructions geared toward data-centric applications. In this paper, we describe the POWER9 core microarchitecture innovations, its new instructions and features, and the exploitation of this new design for computing in the cognitive era.

A highly optimized silicon-on-insulator FinFET technology is utilized for the IBM processor designs in the 14-nm node. This process technology has a number of unique elements that enable these prod...

The latest-generation IBM Z processor provides enhanced performance and compute capacity compared to its IBM z13 predecessor. This paper describes some of the major improvements that include an additional perceptron branch predictor, a completely redesigned translation engine that is tightly integrated into the core pipeline, and an integrated level-1 cache directory and translation lookaside buffer design. Outside of the central processing unit (CPU), the cache sizes have increased on each cache level, and each processor chip now contains 10 CPUs. The system topology has been optimized to improve cache transfer latencies for workloads spanning multiple processor chips. The bus interfaces between the chips have been redesigned to improve peak bus traffic handling. In combination, these enhancements provide significant performance improvements in traditional data-serving workloads, as well as in virtualized Linux environments running database, analytic, and cognitive workloads.

This issue describes the innovative design and technology of the IBM z14, the latest IBM mainframe, with its significant new capabilities, including pervasive encryption, analytics and machine learning, platform simplification, enhanced system capacity and performance, significant reduction in input/output (I/O) latency, data serving, reliability, robustness, and new semiconductor process technology. The topics covered in this issue also consider client value, solutions, and business motivation, along with additional new technologies and innovations across the stack.

Recent advances in technology present an important opportunity in medicine to augment episodic, expert-based observations of patients’ disease signs, obtained in the clinic, with continuous and sensitive measures using wearable and ambient sensors. In Parkinson's disease (PD), such technology-based objective measures have shown exciting potential for passively monitoring disease signs, their fluctuation, and their progression. We are developing a system to passively and continuously capture data from people with PD in their daily lives, and provide a real-time estimate of their motor functions, that is analogous to scores obtained during Part III of the human-administered Movement Disorder Society's Unified Parkinson's Disease assessment (MDS-UPDRS3). Our hypothesis is that complex human movements can be decomposed into movement primitives related to the performance of the MDS-UPDRS3 motor assessment. Toward this hypothesis, we developed a system for integrating and analyzing multiple streams of sensor data collected from volunteers executing the tasks based on the MDS-UPDRS3. In this paper, we show how we can leverage the data collected from MDS-UPDRS3 tasks to develop machine learning models that can identify movement primitives in activities of daily living.

Suicidal ideation detection in online social networks is an emerging research area with major challenges. Recent research has shown that the publicly available information, spread across social media platforms, holds valuable indicators for effectively detecting individuals with suicidal intentions. The key challenge of suicide prevention is understanding and detecting the complex risk factors and warning signs that may precipitate the event. In this paper, we present a new approach that uses the social media platform Twitter to quantify suicide warning signs for individuals and to detect posts containing suicide-related content. The main originality of this approach is the automatic identification of sudden changes in a user's online behavior. To detect such changes, we combine natural language processing techniques to aggregate behavioral and textual features and pass these features through a martingale framework, which is widely used for change detection in data streams. Experiments show that our text-scoring approach effectively captures warning signs in text compared to traditional machine learning classifiers. Additionally, the application of the martingale framework highlights changes in online behavior and shows promise for detecting behavioral changes in at-risk individuals.