Articles tagged with Generative Ai
A new study by Bar-Ilan University finds AI-powered tools are changing how students search for knowledge, especially in non-English-speaking countries. The research suggests that generative AI is reducing the 'language tax' faced by millions of students by making complex concepts more accessible in their native language.
Insilico Medicine introduces the Virtual Aging Cell (VAC) platform, which leverages multi-agent AI to simulate cellular processes, including differentiation, reprogramming, and aging. The VAC platform aims to support target identification, cellular fate intervention, novel drug discovery, and geroscience research.
A longitudinal study of Reddit posts since 2022 reveals that trust in generative AI is generally higher than distrust, with 31% of posts expressing trust and 26% expressing distrust. The study's findings suggest that attitudes toward AI have remained divided over the past four years.
Insilico Medicine has nominated ISM0900, a potent and selective oral Lp(a) inhibitor, as a preclinical candidate for managing cardiovascular risks. The compound demonstrates rapid onset, excellent oral pharmacokinetics, and superior efficacy in reducing Lp(a) levels.
Researchers combined AI, genetics, and gut microbiome analysis to shed light on intestinal fibrosis in Crohn's disease. They identified a shared set of 43 key genes linked to disease progression and found that bowel fibrosis is driven by ongoing immune activation, damage to the intestinal lining, and changes in gut bacteria.
A new study found that AI-assisted proposals are four percentage points more likely to receive funding from the NIH. However, these proposals tend to resemble existing funded ideas, raising concerns about the potential for AI to steer scientific funding toward safer research.
A new review article discusses how artificial intelligence can predict disease trajectories and enable precision medicine strategies for inflammatory bowel disease. AI-based systems can standardize interpretation of endoscopic images, detect mucosal healing, and support recognition of dysplasia in patients with long-standing colitis.
Hyunsoo Lee, an SNU undergraduate, presents research in generative visual computing at leading conferences NeurIPS, CVPR, and ECCV. His work spans image editing, human motion, and 3D content generation, leveraging pretrained generative models to produce consistent outputs.
Researchers developed a method for generating personality assessment questionnaires with ChatGPT from any source text, including the DSM-5 and an astrology textbook. The generated questionnaires showed high internal consistency within personality clusters and could predict life outcomes like depression, anxiety, and well-being at level...
A new study found that six leading AI models exhibit a strong masculine bias in generating stories about animal characters, with only 2% being female. The models use neutral pronouns or avoid them altogether to reduce gender bias, but ultimately erase non-masculine identities.
ISM9077 boasts potent inhibition across species, excellent exposure and permeability traits, and a favorable safety profile in preclinical studies. It modulates pathological inflammation, supporting broad indication expansion into therapeutic areas like aging and neurodegenerative diseases.
Researchers have developed a new photonic architecture that enables scalable spatiotemporal interleaving networks for high-density integrated photonic convolution. The SPIN (Spatiotemporal Photonic Interleaving Network) framework reduces waveguide complexity and increases programmability in wavelength-domain interleaving, enabling comp...
Tianjun Sun's research develops better ways to measure human behavior and abilities, ensuring AI systems are accurate, fair, and trustworthy. Her work combines psychological measurement with AI, aiming to ground AI assessments in scientific standards.
A WVU study found that ChatGPT-5 Pro can generate realistic psychiatry vignettes with strong diagnostic details but emphasizes the need for human-centered approach to ensure patient safety. The researchers recommend incorporating these vignettes into digital psychiatry curricula with faculty moderation and safeguards.
A collaborative study between Insilico Medicine and top research institutions uses AI to identify therapeutic targets for IP-SNSCC, a rare and aggressive subset of head and neck cancers. The study provides the first comprehensive molecular atlas of this disease, enabling future translational research.
Researchers at Harvard John A. Paulson School of Engineering and Applied Sciences developed an AI recommendation model that incorporates reinforcement learning to adjust to the uniqueness of each user. This approach improved human-AI performance over traditional one-size-fits-all decision support.
A study published in Judgement and Decision Making found that participants gave highest ratings to stories generated by AI when attributed to humans. This bias towards narratives written by real people may be due to the assumption of uniquely human qualities required for creative writing, leading people to underestimate AI's capabilities.
A new project supported by DARPA will study AI systems to determine how to train them to withstand failures, attacks, and unexpected situations. The goal is to develop self-improving AI for safety, enabling AI systems to recognize weaknesses in their reasoning and improve behavior over time.
ISM8969/HT-001 is an orally available, brain-penetrant NLRP3 inhibitor with promising in vitro and in vivo profiles. The clinical trial aims to evaluate its safety, tolerability, and efficacy in patients with Parkinson's disease.
The DDD Benchmark provides an independent, real-world measure of how a model performs in drug discovery. It comprises two evaluation suites: Drug Discovery Foundations and Drug Candidate Essentials.
Researchers developed a novel AI framework that optimizes investment decisions directly while accounting for risk. The study found that conventional forecasting-based approaches were outperformed by the decision-focused model in terms of risk-adjusted performance and wealth accumulation.
A new study by researchers at Johns Hopkins University highlights a significant disconnect between those who use AI health tools and those who create and fund them. The study reveals that key stakeholders have fundamentally different definitions of value, usability, and cost, creating systemic barriers to technology adoption.
Insilico Medicine's Dr. Alex Zhavoronkov will deliver two keynotes on AI-driven drug discovery and anti-aging at Ai4 2026. The panel discussion explores the evolution of AI in medicine, while the solo keynote delves into the company's strategy for breakthrough innovation using frontier AI models.
ISM6331, a novel pan-TEAD inhibitor, has received Fast Track Designation from the FDA for treating adult patients with unresectable malignant pleural mesothelioma. The treatment boasts synergistic anti-tumor effects and potential to overcome drug resistance as combination therapy.
Insilico Medicine presents first-in-human Phase 1 study data for its novel AI-designed pan-TEAD inhibitor ISM6331, targeting complex oncogenic drivers like the Hippo pathway. The study assesses safety, pharmacokinetics, and preliminary antitumor activity in patients with advanced mesothelioma and other solid tumors.
Researchers have developed an AI tool that can detect online propaganda in Kinyarwanda, a Bantu language spoken by 350 million Africans. The dataset, called KinyaProp, provides examples of misinformation in Kinyarwanda for large language models to learn from and recognize.
A growing trend of patients turning to AI chatbots for health advice reflects broader healthcare system strain and patient dissatisfaction with traditional care options. Critical care doctor Robert B. Shpiner argues that addressing underlying structural issues is essential to mitigate AI risks.
A new AI model called Biogeochemistry-Informed Neural Network (BINN) has been developed to advance scientific discovery in agriculture and biogeochemistry. It is 50 times more efficient than its predecessors and can predict biological processes not yet well understood.
Researchers at Harvard John A. Paulson School of Engineering and Applied Sciences have developed a new AI framework called Orla that streamlines building and running AI workflows. In tests, Orla reduced computing costs and response times without sacrificing quality.
The US and China have distinct advantages in different domains, with the US lacking efficient translational infrastructure and China building a more efficient biomedical development engine. The conversation will address areas of collaboration and competition in biopharmaceutical innovation.
A Tulane University team is using AI to discover new superconductors, which could improve the nation's electrical grid, medical imaging, and quantum computing. The project combines high-fidelity calculations, physics-aware AI, and experimental measurements to accelerate discovery.
Insilico Medicine has nominated an AI-driven novel compound ISM9528 for pain management, addressing the $100 billion market challenge. The compound targets Target Z and offers a non-opioid treatment approach with superior efficacy and safety profile.
Researchers developed an AI platform, PeptiVerse, to predict key properties of peptides, enabling early assessment of drug potential. The open-source platform allows users to evaluate ordinary and chemically modified peptides, streamlining the discovery process.
Researchers developed a framework to integrate AI into hospitals, emphasizing patient care, staff experience, and economic sustainability. The Total Mission Value framework aims to ensure high-quality patient care remains the top priority amidst AI's transformative potential.
A new study reveals that AI chatbots' popularity stems from their interactive, personalized, and imaginative nature, allowing users to engage with them as trusted companions. The study warns of the potential dangers of these chatbots, including their ability to persuade users to believe false or unethical information.
At CPIC 2026, Insilico Medicine's Dr. Alex Zhavoronkov and Dr. Feng Ren presented on their approach to AI-driven life sciences, leveraging the Pharma.AI platform for agile translation and closed-loop validation. The duo emphasized the importance of technological breakthroughs and clinical validation in shaping the future of R&D.
A recent study found that patients perceive medical professionals as more credible when AI agrees with their diagnosis. However, disagreement can increase perceptions of medical uncertainty and doctor laziness. The researchers suggest strategies to communicate AI disagreement effectively and reduce patient mistrust.
The European Law Institute has approved its first comprehensive framework for dealing with digital assets and personal digital content after death. The rules provide a dual legal framework recognizing the need for protection, privacy, and dignity in digital inheritance.
A study published by University of Cincinnati associate professor Nelly Elsayed highlights the importance of human review in AI-driven physicians' documentation to ensure transparency, privacy, and reliability. The research also emphasizes the need for clinicians to be trained on software before adoption to curb errors.
A team of researchers from the University of Cambridge and UC Santa Barbara developed 'adversarial' mathematical systems to map out where AI prediction breaks down. They identified two main reasons why machine learning fails: algorithmic limitations and hidden patterns in complex systems.
The strategic alliance combines Insilico's Pharma.AI platform with Bora's global capabilities to explore a next-generation drug innovation model. The partnership aims to accelerate Bora's transition towards AI-driven drug discovery and development, leveraging Insilico's comprehensive research and development strategies.
Researchers developed an auditing technique to test generative AI models for malicious capabilities, without prompting them for illegal outputs. The new approach identifies model variations that can generate CSAM with 100% accuracy.
The collaboration aims to advance co-development of R&D programs by combining Insilico's validated AI platform with CMS's therapeutic expertise. The partnership enables the development of innovative drugs for central nervous system conditions, streamlining the process and accelerating delivery of clinically meaningful innovations.
Insilico Medicine will deliver a keynote speech and participate in a panel discussion on the future of drug discovery with generative AI. The company showcases its innovative approach to accelerating drug development, compressing timelines and enabling sustainable growth.
A study from Technical University of Munich found that people perceive AI hiring decisions as unfair when the avatar resembles them in terms of gender or skin color. After receiving a rejection, trust in AI is shaken if the avatar's appearance differs from their own.
Deep learning models accelerate drug design, predict chemical interactions, and engineer stable candidates. AI-powered simulations optimize dosimetry, predicting biodistribution and generating patient-specific digital twins for individualized treatment planning.
Researchers at the University of Pennsylvania and Chinese University of Hong Kong created TD3B, an AI framework guiding peptide generation toward candidates predicted to have a desired effect. The tool predicts binding likelihood and determines activation or deactivation of associated cellular machinery.
A team of researchers, led by Ying Zhang, has developed artificial intelligence-driven tools to identify and attack software vulnerabilities. By teaching AI to generate proof-of-concept exploits, developers can see exactly how attackers could exploit known flaws, motivating them to fix issues before malicious actors do.
A team from Singapore University of Technology and Design has developed an AI agent that uses artificial intelligence to help patients make life-or-death decisions. The system, called ACPAgent, was tested with 15 participants who agreed with its recommendations in 86.7% of cases, but also showed limitations in handling high-subjectivit...
Research reveals that digital romances can mimic human stages, including exploration, emotional connection, break-ups, and even simulated weddings and pregnancies. Participants shared intimate details and attributed autonomy to their AI partners, raising concerns about privacy and data protection.
A new UGA study found that AI chatbots often provide recommendations that are inconsistent across generative AI platforms and may vary by sociodemographic groups. The researchers created three hypothetical scenarios of people who needed advice on emergency funds, investment portfolios, and retirement savings, which were entered into se...
Researchers warn that AI faces are becoming increasingly trustworthy, with the latest diffusion model outperforming earlier models. This poses significant risks of online fraud and erosion of trust in society, as people become more susceptible to fake faces used for nefarious purposes.
Rentosertib is a potentially first-in-class oral small-molecule inhibitor targeting TNIK for the treatment of idiopathic pulmonary fibrosis. The Phase III clinical trial will evaluate the efficacy and safety of once-daily Rentosertib administered over 52 weeks.
The collaboration aims to develop AI-powered ground systems that can assist operators with routine satellite operations, mission scheduling, and data analysis. The partnership seeks to automate routine tasks with human oversight, enabling more efficient management of larger satellite fleets.
A new learning-based adaptive tuning method integrates chaotic search with particle swarm optimization to improve stability and solution quality in chaotic search algorithms. The approach consistently achieves better results than conventional methods, providing a practical means of enhancing the performance of chaotic search.
A large-scale study of an online patient portal shows that AI-generated responses can introduce errors and extraneous details, leading to increased editing time for physicians. Adapting AI to individual physician communication styles can improve accuracy by 33% and reduce editing by 26%.
Insilico Medicine has expanded its generative AI capabilities through a strategic collaboration with Liquid AI, yielding models optimized for retrosynthesis and multi-omics target discovery. The company's platform upgrades enable enterprise-grade automation, parallel simulation, and precision sifting in biologics design.
A new study reveals that large language models can systematically alter the direction of users' messages on contested topics, even when instructed to preserve the original meaning. The researchers show how these small changes can accumulate and gradually influence broader public opinion over time.
Insilico Medicine is leveraging its end-to-end Pharma.AI platform to drive clinical validation in AI drug discovery, with a diversified pipeline featuring over 40 projects. The company has nominated 31 development candidates and achieved positive results in a Phase IIa clinical trial for Rentosertib, an AI-discovered TNIK inhibitor for...
The collaboration aims to identify clinically differentiated drug candidates using Insilico's generative AI and Takeda's global development capabilities. The partnership seeks to deliver meaningful treatment options for patients and support Takeda's transition to an AI-native discovery model.