When Biology Meets Medicine: A Perspective on Foundation Models
Artificial intelligence, and foundation models in particular, are transforming life sciences and medicine. This perspective reviews biological and medical foundation models across scales, highlighting key challenges in data availability, model evaluation, and architectural design.
Kunying Niu +3 more
wiley +1 more source
In silico design of novel precision vaccine targeting sclerostin epitopes for osteoporosis prevention and treatment. [PDF]
Luo J +5 more
europepmc +1 more source
AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley +1 more source
In Silico Design, Synthesis, and Antibacterial Evaluation of Allyl Esters of Salicylic and Acetylsalicylic Acid and Their Copolymers. [PDF]
Garaev E +3 more
europepmc +1 more source
AS‐pHopt: An Optimal pH Prediction Model Enhanced by Active Site of Enzymes
To address the low accuracy of enzyme optimal pH (pHopt) prediction, this study develops active site‐based pHopt (AS‐pHopt), a prediction model enhanced by active site information and pseudo‐label prediction. Integrating key structural and physicochemical features affecting enzyme pHopt, AS‐pHopt uses Evolutionary Scale Modeling (ESM)‐2 with active ...
Wenxiang Song +6 more
wiley +1 more source
In Silico Design of gRNA for CRISPR System for Detection of Multidrug Resistant Tuberculosis Using Indian Mycobacterium tuberculosis Genomes: A Computational Study. [PDF]
Mittal A, Manna S, Nelson V, Ladha N.
europepmc +1 more source
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi +2 more
wiley +1 more source
<i>In silico</i> design and binding mechanism of UBR1 E3 ligase recruiters. [PDF]
Maria-Solano MA, Lazim R, Choi S.
europepmc +1 more source
This research demonstrates that the combination of domain knowledge–based multiple regression, multi‐objective Bayesian optimization, and generative models is a suitable prediction tool for candidates of high refractive index polymers, even with the constraints in the model trained on limited data. The experimental validation can reproduce the proposed
Takuya Yokoo +3 more
wiley +1 more source
A novel multi-epitope mRNA vaccine against colorectal cancer: in silico design and immune efficacy profiling. [PDF]
Wang L, Zhou X, Wei Y, Lin J.
europepmc +1 more source

