14,070 papers across 0 tags, ranked by community signal and explained.
This review aims to provide a thorough overview of AI's role in drug discovery . AI can aid in various stages of drug discovery in various ways, including disease identification, t…
Current computational approaches for drug design typically focus on generating molecules conditioned on specific targets or general molecular properties, often neglectin
The concept of a digital materials ecosystem represents a new paradigm in materials research, where data, theory, and automation are integrated into a unified and iterative framewo…
The coronavirus disease 2019 (COVID-19) pandemic has stimulated extensive endeavors toward the development of therapeutic interventions targeting severe acute respiratory syndrome …
This review presents a comprehensive perspective on how AI and big data strategies can transform the understanding and design of the electrode–electrolyte interphases (EEI) in rech…
Generative models have emerged as a powerful paradigm for solving physics systems and modeling complex spatiotemporal dynamics. However, achieving high physical accuracy w
Deep generative models have emerged as powerful computational engines for de novo molecular design, enabling efficient exploration of a vast chemical space that remains inaccessibl…
Integrating artificial intelligence (AI) into drug discovery revolutionizes pharmaceutical research by significantly accelerating the identification, optimization, and development …
De novo protein design has enabled the creation of proteins with diverse functionalities that are not found in nature. Despite recent advances, experimental success rates remain in…
Abstract We introduce Protenix-v1 (PX-v1), the first fully open-source structure prediction model to attain superior performance to AlphaFold3 while strictly adhering to the same t…
Self-assembled monolayers (SAMs) have precipitated a paradigm shift in the design of hole transport layers (HTLs) for p-i-n perovskite solar cells, emerging as the cornerstone of m…
We introduce InternAgent-1.5, a unified system designed for end-to-end scientific discovery across computational and empirical domains. The system is built on a structured architec…
Proteolysis-targeting chimeras (PROTACs) and molecular glues promote targeted protein degradation by recruiting an E3 ligase to proteins of interest (POIs). An accurate 3D structur…
Computational blind challenges offer critical, unbiased opportunities to assess and accelerate scientific progress, as demonstrated by a breadth of breakthroughs over the past deca…
Artificial intelligence (AI) is reshaping drug discovery by accelerating timelines and reducing costs, yet its impact remains constrained by a persistent gap between computational …
This review examines the application of Artificial Intelligence (AI) in the discovery and optimisation of neuroprotective natural products (NPs) for neurodegenerative diseases (NDD…
We present PocketXMol, an atom-level model that unifies generative tasks related to protein pocket interactions. Using atomic prompts as task specifications, PocketXMol supports va…
Drug discovery is still an expensive and time-consuming enterprise, a majority of clinical trial failures being traced to inaccurate predictions of molecular properties. To address…
Proteolysis-targeting chimeras (PROTACs) represent a transformative strategy in drug discovery, enabling the selective degradation of target proteins rather than merely inhibiting …
Direct‐to‐biology (D2B) has emerged as a transformative concept in early drug discovery, defined by the direct on‐target screening of crude reaction mixtures without prior purifica…
Abstract Learning and memory, central to cognitive function and critical targets for drug discovery in neurological disorders, fundamentally rely on synaptic plasticity, such as lo…
Fragment-based Drug Discovery (FBDD) is a proven methodology for the discovery of new therapeutics. After the identification of small molecular fragments, subsequent steps are guid…
The pharmaceutical industry is undergoing a crucial transformation toward sustainability, driven by the urgent need to reduce environmental harm while maintaining innovation in dru…