#genetic-optimization

#genetic-optimization

OpenAI has tuned the model to be more skeptical, so it's more likely to tell you when something is a bad drug target. The former was defined as being able to work through complex, multi-step processes, while the latter was derived from the model's performance on a handful of benchmarks.

Amazon Bio Discovery enables scientists to run complex computational workflows through more than 40 AI-specialized foundational models, trained on a wide range of biological datasets. These models generate and evaluate potential drug molecules, alongside AI agents that help scientists to select models, optimize inputs and evaluate candidates according to their research.

Incyte tops this list due to its rare combination of commercial scale, cash generation, and pipeline depth. The company posted FY2025 revenue of $5.14 billion, up 21.2% YoY, anchored by Jakafi generating $828.2 million in Q4 2025 alone (+7% YoY) and Opzelura delivering $207.3 million (+28% YoY). With $3.58 billion in cash and 14 pivotal clinical trials underway, Incyte offers an acquirer immediate revenue, margin expansion potential, and a deep oncology pipeline spanning KRASG12D, CDK2 inhibition, and mutCALR.

GPT-Rosalind is designed to support evidence synthesis, hypothesis generation, experimental planning, and multi-step scientific workflows across biochemistry, genomics, and protein engineering.

Using CRISPR-Cas9 and adeno-associated virus (AAV)-mediated homology-directed repair, we targeted CAR integration into the endogenous human TCR alpha locus (TRAC). TRAC-CAR T cells display dynamic CAR expression that delays exhaustion and improves tumour control in xenograft and immunocompetent models. This work has been critical for the development of allogeneic CAR T cell therapy, as it disrupts the TCR after transgene insertion—a necessary step to limit graft-versus-host disease.

Before treatment began, participants underwent neuroimaging. Instead of relying on a single modality, the researchers fused structural connectivity (how regions are physically wired) with functional connectivity (how regions co-activate at rest). The goal was not to throw every possible feature at a black box, but to learn a constrained pattern-what the authors call structure-function "covariation"-that carries the most predictive signal for outcome. In other words, the model tries to find the smallest set of connections that meaningfully forecasts symptom change.

Biology is undergoing a transformation. After centuries of studying life as it evolves naturally, researchers are now using a combination of computation and genome engineering to intervene, generating new proteins and even whole bacteria from scratch. The use of artificial-intelligence tools to design biological components, an approach known as generative biology, is set to turbocharge this area of research. Just last year, scientists used AI-assisted design to produce artificial genes that can be expressed in mammalian cells.

Martschenko's argument is largely that genetic research and data have almost always been used thus far as a justification to further entrench extant social inequalities. But we know the solutions to many of the injustices in our world-trying to lift people out of poverty, for example-and we certainly don't need more genetic research to implement them. Trejo's point is largely that more information is generally better than less.

Scientists in the laboratory of Rendong Yang, PhD, associate professor of Urology, have developed a new large language model that can interpret transcriptomic data in cancer cell lines more accurately than conventional approaches, as detailed in a recent study published in Nature Communications. Long-read RNA sequencing technologies have transformed transcriptomics research by detecting complex RNA splicing and gene fusion events that have often been missed by conventional short-read RNA-sequencing methods.