Virtual screening articles within Nature Communications

Featured

• Article
  15 September 2023 | Open Access

  First fully-automated AI/ML virtual screening cascade implemented at a drug discovery centre in Africa

  Streamlined data-driven drug discovery remains challenging, especially in resource-limited settings. Here, the authors present ZairaChem, an AI/ML tool that streamlines QSAR/QSPR modelling, implemented for the first time at the H3D Centre in South Africa.

  • Gemma Turon
  • , Jason Hlozek
  •  & Miquel Duran-Frigola

• Article
  12 June 2023 | Open Access

  Machine learning optimization of candidate antibody yields highly diverse sub-nanomolar affinity antibody libraries

  Therapeutic antibody discovery is time and cost-intensive. Here, the authors develop a machine learning-driven method enabling accelerated design of large and diverse single-chain variable fragments with high binding efficiency, especially at high levels of diversity.

  • Lin Li
  • , Esther Gupta
  •  & Matthew E. Walsh

• Article
  10 June 2023 | Open Access

  Discovery of senolytics using machine learning

  Cellular senescence is involved in many disease processes but few senolytic compounds are currently known. Here, the authors report the discovery of three senolytics using machine learning models trained solely on published data, with large reductions in drug screening costs.

  • Vanessa Smer-Barreto
  • , Andrea Quintanilla
  •  & Diego A. Oyarzún

• Article
  16 May 2023 | Open Access

  Identification of indocyanine green as a STT3B inhibitor against mushroom α-amanitin cytotoxicity

  There is currently no specific antidote for death cap mushroom poisoning treatment. Here, the authors identify STT3B as a druggable target and show that indocyanine green is a STT3B inhibitor that can block α-amanitin toxicity in cell lines, liver organoids and mice.

  • Bei Wang
  • , Arabella H. Wan
  •  & Qiao-Ping Wang

• Article
  12 September 2022 | Open Access

  Identification of a binding site on soluble RANKL that can be targeted to inhibit soluble RANK-RANKL interactions and treat osteoporosis

  Huang et al. discover a binding site on soluble RANKL that is not found on its membrane-bound homologue. A drug screening identified a small molecule (S3-15) that can target this binding site and has anti-osteoporotic but not immunosuppressive effects.

  • Dane Huang
  • , Chao Zhao
  •  & Jun Xu

• Article
  17 November 2020 | Open Access

  Computationally predicting clinical drug combination efficacy with cancer cell line screens and independent drug action

  Computational models that can predict drug combination efficacy are often based on drug synergy. Here, the authors develop a different approach to computationally predict the efficacy of drug combinations using monotherapy data from high-throughput cancer cell line screens.

  • Alexander Ling
  •  & R. Stephanie Huang

• Article
  07 August 2020 | Open Access

  Structure-based evolution of a promiscuous inhibitor to a selective stabilizer of protein–protein interactions

  Small molecule stabilizers of protein–protein interactions hold great therapeutic potential. Based on virtual screening and molecular docking, the authors here develop a strategy to evolve weak, promiscuous inhibitors of 14-3-3 interactions into selective stabilizers of the 14-3-3/ChREBP complex.

  • Eline Sijbesma
  • , Emira Visser
  •  & Christian Ottmann

• Article
  09 June 2017 | Open Access

  A covalent PIN1 inhibitor selectively targets cancer cells by a dual mechanism of action

  PIN1 is a promising therapeutic target for cancer treatment. In this study, the authors identify a covalent inhibitor of PIN1 with anti-tumour and anti-metastatic properties thanks to PIN1 inactivation and to the release, after binding to PIN1, of a quinone-mimicking compound that elicits reactive oxygen generation and causes DNA damage.

  • Elena Campaner
  • , Alessandra Rustighi
  •  & Giannino Del Sal