Life Sciences

The article proposes a framework for natural product drug discovery that translates evidence into auditable experimental actions through a four-tier architecture (source authentication, reproducible chemical fingerprinting, orthogonal prioritization, definitive characterization) with documented decision gates, and an 'evidence ladder' that separates molecular-identification confidence from chemical novelty, biological novelty and translational relevance, addressing three recurring gaps: incomplete integration of sample metadata, conflation of analytical detection with molecular novelty, and lack of explicit decision thresholds.

This review systematically surveys AI-driven drug-target interaction (DTI) prediction, starting from classical molecular binding theories (lock-and-key, induced fit, conformational selection) and summarizing task settings such as binary interaction classification, binding affinity regression, and multitask prediction with uncertainty assessment; it organizes multimodal representations for drugs and target proteins (molecular sequences, graph structures, 3D conformations, physicochemical properties, biological perturbation profiles, protein sequences and structures, biomedical knowledge networks) together with interaction labels and auxiliary biomedical data, compares representative approaches across orthogonal dimensions including input representation, encoder architecture, interaction-mod

The work presents LinkLlama, a fine-tuned Meta Llama 3 model supervised on a curated corpus of drug-like molecules from ChEMBL that accepts natural language prompts specifying geometric constraints such as distances and angles alongside physicochemical targets like Lipinski's rules and rotatable bond limits to generate tailored molecules for input fragments; benchmarking on the ZINC and HiQBind data sets shows competitive geometric fidelity relative to strictly 3D-aware models together with an approximately two-fold increase in the proportion of chemically reasonable designs, rising from 35% to over 80% under structural filters including PAINS, non-drug-like chemical patterns, and complex ring systems, with versatility illustrated through small-molecule scaffold hopping and PROTAC linker d

This review examines how microwell platforms determine the information obtainable from individual cells through cell-loading strategies, well geometry, platform architecture and material selection, reviews their applications in cellular behaviour and cell-cell interactions, secretome analysis, genomic and transcriptomic profiling, and drug screening and precision medicine, and distinguishes AI approaches directly demonstrated in microwell-based studies from those still prospective, indicating that linking microwell engineering with AI-driven analysis can move microwell-based single-cell research from measurement towards prediction and autonomous discovery.

This study treats mRNA stability as a tunable codon-optimization design parameter: it built a combinatorial library of synonymous coding-sequence variants of an IgG1 light chain integrated as single copies at a defined genomic locus in CHO cells, used steady-state mRNA abundance quantified by deep sequencing of gDNA and mRNA as a proxy for stability, trained a machine-learning model predicting mRNA abundance from coding sequence using embeddings from a pre-trained nucleotide transformer, and incorporated this predictor with established translational metrics into a genetic algorithm for multi-objective codon optimization; as proof-of-concept with Trastuzumab-encoding sequences, high-abundance designs raised intracellular mRNA by 41%, protein titer by 59%, and cell-specific productivity by 8

Pro-GAT is a graph attention-based framework for geometry-preserving molecular graph repair that operates on chemically disconnected diffusion-generated PROTAC candidates by predicting bounded coordinate corrections and constrained atom-type modifications through geometry-aware graph attention layers; it recovers 31.58% of disconnected structures in the DiffPROTAC test set, and when combined with DiffPROTAC and DiffLinker fine-tuned on PROTAC-specific data, it improves the proportion of chemically valid candidates in the aggregated output from 76.70% to 83.92% and from 63.16% to 68.73%, while maintaining uniqueness levels of 80.18% and 63.

This study introduces a new avenue for foundation model pretraining: supervised pretraining on low-noise, calculable molecular descriptors to obtain rich, highly transferable molecular representations, demonstrated with CheMeleon, an O(10M) parameter foundation model; across 58 benchmark data sets spanning properties relevant to small-molecule drug discovery and sourced from the industry-led Polaris benchmarking initiative, CheMeleon enables directed message-passing neural networks to finally exceed classical methods in the low-data regime, outperforms classical baselines such as Random Forest on molecular fingerprints and descriptors as well as existing foundation models under rigorous statistical comparisons, and the model and pretraining framework are open-sourced.