drug-discovery

Published on September 12, 2026 2:47 AM GMT The advancement of frontier models is beginning to change our understanding of how we understand science and scientific discoveries. Scientists are now able to solve complex problems using AI. AI models have now been used to identify novel drug targets, develop novel drugs. For example, AI model was used to design rentosertib, a drug candidate for idiop…

UF’s AI model outperformed more than 350 submissions and ranked No. 1 among all entries developed exclusively using publicly available data.

OutSee, a genomics and drug discovery company pioneering a unique AI-based predictive approach to uncover new drug targets, today announced it has been awarded funding through Innovate UK's Investor Partnership Grant, jointly provided by Innovate UK and Empirical Ventures.

Insilico’s AI-generated drug rentosertib showed signs linked to younger biological age, raising new questions about where AI drug discovery could lead next.

Sponsored content brought to you by Artificial intelligence is dramatically accelerating early drug discovery. Models can screen chemical space, predict structures, optimize properties, and propose new molecules at speeds that were unimaginable […] The post AI Drug Discovery Hits a New Bottleneck: Experimental Validation appeared first on GEN - Genetic Engineering and Biotechnology News .

Foundation models will change the way we study the natural, applied and clinical sciences, but only under certain conditions, and not in the way that some AI leaders claim.

Artificial intelligence was used to help develop a drug candidate, rentosertib, for a rare lung condition. Its maker says the drug also seems to reduce the biological hallmarks of age.

Insider Brief An artificial intelligence-designed drug for a deadly lung disease produced protein changes associated with lower biological age, offering early evidence that AI-discovered medicines could be evaluated for effects beyond the diseases they were created to treat. Researchers reported in Nature Biotechnology that rentosertib, an experimental treatment for idiopathic pulmonary fibrosis,…

Insilico Medicine ("Insilico"), a clinical-stage company focused on generative AI-driven drug discovery and development, today announced the publication of a new study conducted in collaboration with an international team of scientists from Harvard Medical School, Stanford University, The Broad Institute, RWTH Aachen University, Peking University and Westlake University.

Nature Reviews Drug Discovery, Published online: 02 September 2026; doi:10.1038/d41573-026-00146-x Click chemistry platform enhances ADC tumour targeting

Emerging and re-emerging infections expose a persistent mismatch between the speed of pathogen evolution and the pace of therapeutic development. Conventional two-dimensional cultures are scalable but poorly reproduce tissue architecture, whereas animal models may not capture human-specific tropism, immunity, pharmacokinetics, or toxicity. Human organoids and organ-on-chip systems offer complemen…

[Cell Reports] Researchers developed a patient-derived liver cancer organoid platform for high-throughput drug screening, identifying promising drug candidates and combination therapies across heterogeneous tumors.

(MIT Technology Review) – Patents can only name humans as inventors. For now. Insilico leads a pack of companies using AI to rapidly come up with drug ideas humans might never think of, potentially speeding the race to new cures. … Read More

Small molecules are the basis of most medicines and crop-protection products in use today, and account for the

Quinazoline scaffolds are widely recognized in drug discovery for their diverse pharmacological profiles, and several quinazoline derivatives have been reported as phosphodiesterase 7A (PDE7A) inhibitors with potential anti-inflammatory effects. This study aimed to synthesize novel quinazoline derivatives, evaluate their anti-inflammatory activity and investigate their computationally predicted b…

Drug-target interaction prediction can help researchers explain mechanisms of action, identify candidate targets, reposition existing medicines and prioritize compounds before costly laboratory work.

Artificial intelligence is reshaping one of drug discovery's most consequential questions: which compounds interact with which biological targets, and how strongly. A new review maps the fast-moving landscape of artificial intelligence (AI)-driven drug-target interaction (DTI) prediction, showing how the field has progressed from hand-crafted molecular features to deep learning, graph-based model…

There has been a growing focus on ‘molecular chameleons’ possessing structural flexibility; compounds that can dynamically adjust their conformations depending on the characteristics of the medium to either conceal or reveal polar parts in aqueous/lipidic environments. Drug discovery has shifted in the past few decades toward more complex molecules, such as cyclic and macrocyclic peptides, as wel…