Latest AI and machine learning research in prescriptions for healthcare professionals.
Side effects caused by drug combinations pose a major challenge in healthcare. Knowledge of these side effects is limited because often they are not detected in clinical trials, which typically involve a restricted number of participants and tested drug combinations. We introduce DCSE (Drug Combinations Side Effects), a novel machine learning method for predicting polypharmacy side effects. DCSE l...
Hallmark gene mutations shape cancer cell vulnerabilities and inform drug discovery1–3. A systematic map of hallmark gene mutation-defined cancer dependencies and therapeutic responses is essential to uncover novel targets and refine therapeutic strategies. Here, we present the first pan-cancer blueprint of hallmark vulnerabilities, systematically linking hallmark gene mutation markers to cancer c...
The modern AI models promise decoding of the genomic landscape that holds, in principle, the information required for rational therapeutic design. Gen...
Predicting enzyme–small molecule interactions is critical for drug discovery and generally understanding the biochemical processes of life. While rece...
Since the clinical introduction of antibiotics in the 1940s, antimicrobial resistance (AMR) has become an increasingly dire threat to global public he...
Identifying protein binding sites is central to drug discovery, yet many computational approaches still trade off precision, recall, or throughput whe...
Determining precise drug concentrations to inhibit cancer cell growth is a critical but resource-intensive challenge, especially for combinations requ...
Post-marketing surveillance is crucial for drug safety, yet the tools of pharmacovigilance rely solely on text-based data that may limit the applicabi...
Secondary metabolites in plants have various physiological functions, including antioxidant and antibacterial activities. Previous studies have sugges...
Amyotrophic lateral sclerosis (ALS), a progressive neuromuscular degenerative disease, rapidly impairs communication within years of onset. This loss ...
Artificial intelligence plays an ever-greater role in preclinical drug development, ranging from target identification and molecule design to ADME-Tox...
The human brain’s capacity to imagine visual scenes from memory is thought to rely on the medial temporal subsystem of the default mode network (MT-DM...
The light/dark box test can be used to assess visual function in rodents based on their spontaneous behavior in response to light. Commonly used assay...
Proteins function through dynamic interactions with other proteins in cells, forming complex networks fundamental to cellular processes. While high-re...
Attention-deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder in the U.S., and the stimulant and nonstimulant medicat...
Manual annotation of drone imagery is labour-intensive and prone to observer bias, particularly when applied to large datasets across varied environme...
RNA interactions with proteins, other RNA molecules, and DNA play essential roles in numerous cellular processes and underpin a wide range of therapeu...
Drug-target interaction prediction is an important task in computational drug discovery. To address the limitations of existing graph neural network a...
Previously, we proposed a double-point mutation (DPM) strategy involving the simultaneous substitution of two amino acids to optimize antibodies. By s...
The process of lead optimization in drug discovery is a complex, multi-objective challenge that remains a major bottleneck in the development of new t...