The Art and Science of Molecular Docking

Annual Review of Biochemistry, Год журнала: 2024, Номер 93(1), С. 389 - 410

Опубликована: Апрель 10, 2024

Molecular docking has become an essential part of a structural biologist's and medicinal chemist's toolkits. Given chemical compound the three-dimensional structure molecular target—for example, protein—docking methods fit into target, predicting compound's bound binding energy. Docking can be used to discover novel ligands for target by screening large virtual libraries. also provide useful starting point structure-based ligand optimization or investigating ligand's mechanism action. Advances in computational methods, including both physics-based machine learning approaches, as well complementary experimental techniques, are making even more powerful tool. We review how works it drive drug discovery biological research. describe its current limitations ongoing efforts overcome them.

Язык: Английский

---

Structure prediction of protein-ligand complexes from sequence information with Umol

Nature Communications, Год журнала: 2024, Номер 15(1)

Опубликована: Май 28, 2024

Abstract Protein-ligand docking is an established tool in drug discovery and development to narrow down potential therapeutics for experimental testing. However, a high-quality protein structure required often the treated as fully or partially rigid. Here we develop AI system that can predict flexible all-atom of protein-ligand complexes directly from sequence information. We find classical methods are still superior, but depend upon having crystal structures target protein. In addition predicting structures, predicted confidence metrics (plDDT) be used select accurate predictions well distinguish between strong weak binders. The advances presented here suggest goal AI-based one step closer, there way go grasp complexity interactions fully. Umol available at: https://github.com/patrickbryant1/Umol .

Язык: Английский

---

The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins

Nature Chemical Biology, Год журнала: 2024, Номер 20(8), С. 950 - 959

Опубликована: Июнь 21, 2024

Язык: Английский

---

Artificial intelligence alphafold model for molecular biology and drug discovery: a machine-learning-driven informatics investigation

Molecular Cancer, Год журнала: 2024, Номер 23(1)

Опубликована: Окт. 5, 2024

AlphaFold model has reshaped biological research. However, vast unstructured data in the entire field requires further analysis to fully understand current research landscape and guide future exploration. Thus, this scientometric aimed identify critical clusters, track emerging trends, highlight underexplored areas by utilizing machine-learning-driven informatics methods. Quantitative statistical reveals that is enjoying an astonishing development trend (Annual Growth Rate = 180.13%) global collaboration (International Co-authorship 33.33%). Unsupervised clustering algorithm, time series tracking, impact assessment point out Cluster 3 (Artificial Intelligence-Powered Advancements for Structural Biology) greatest influence (Average Citation 48.36 ± 184.98). Additionally, regression curve hotspot burst "structure prediction" (s 12.40, R2 0.9480, p 0.0051), "artificial intelligence" 5.00, 0.8096, 0.0375), "drug discovery" 1.90, 0.7987, 0.0409), "molecular dynamics" 2.40, 0.8000, 0.0405) as core hotspots driving frontier. More importantly, Walktrap algorithm prediction, artificial intelligence, molecular (Relevance Percentage[RP] 100%, Development Percentage[DP] 25.0%), "sars-cov-2, covid-19, vaccine design" (RP 97.8%, DP 37.5%), "homology modeling, virtual screening, membrane protein" 89.9%, 26.1%) are closely intertwined with but remain underexplored, which implies a broad exploration space. In conclusion, through methods, offers objective comprehensive overview of research, identifying clusters while prospectively pointing areas.

Язык: Английский

---

Structure-based virtual screening of vast chemical space as a starting point for drug discovery

Current Opinion in Structural Biology, Год журнала: 2024, Номер 87, С. 102829 - 102829

Опубликована: Июнь 6, 2024

Structure-based virtual screening aims to find molecules forming favorable interactions with a biological macromolecule using computational models of complexes. The recent surge commercially available chemical space provides the opportunity search for ligands therapeutic targets among billions compounds. This review offers compact overview structure-based screens vast spaces, highlighting successful applications in early drug discovery therapeutically important such as G protein-coupled receptors and viral enzymes. Emphasis is placed on strategies explore ultra-large libraries synergies emerging machine learning techniques. current opportunities future challenges are discussed, indicating that this approach will play an role next-generation pipeline.

Язык: Английский

---

Overview of AlphaFold2 and breakthroughs in overcoming its limitations

Computers in Biology and Medicine, Год журнала: 2024, Номер 176, С. 108620 - 108620

Опубликована: Май 15, 2024

Язык: Английский

---

Artificial intelligence for drug repurposing against infectious diseases

Artificial Intelligence Chemistry, Год журнала: 2024, Номер 2(2), С. 100071 - 100071

Опубликована: Июнь 12, 2024

Traditional drug discovery struggles to keep pace with the ever-evolving threat of infectious diseases. New viruses and antibiotic-resistant bacteria, all demand rapid solutions. Artificial Intelligence (AI) offers a promising path forward through accelerated repurposing. AI allows researchers analyze massive datasets, revealing hidden connections between existing drugs, disease targets, potential treatments. This approach boasts several advantages. First, repurposing drugs leverages established safety data reduces development time costs. Second, can broaden search for effective therapies by identifying unexpected new targets. Finally, help mitigate limitations predicting minimizing side effects, optimizing repurposing, navigating intellectual property hurdles. The article explores specific strategies like virtual screening, target identification, structure base design natural language processing. Real-world examples highlight AI-driven in discovering treatments

Язык: Английский

---

AlphaFold accelerated discovery of psychotropic agonists targeting the trace amine–associated receptor 1

Science Advances, Год журнала: 2024, Номер 10(32)

Опубликована: Авг. 7, 2024

Artificial intelligence is revolutionizing protein structure prediction, providing unprecedented opportunities for drug design. To assess the potential impact on ligand discovery, we compared virtual screens using structures generated by AlphaFold machine learning method and traditional homology modeling. More than 16 million compounds were docked to models of trace amine-associated receptor 1 (TAAR1), a G protein-coupled unknown target treating neuropsychiatric disorders. Sets 30 32 highly ranked from model screens, respectively, experimentally evaluated. Of these, 25 TAAR1 agonists with potencies ranging 12 0.03 μM. The screen yielded more twofold higher hit rate (60%) discovered most potent agonists. A agonist promising selectivity profile drug-like properties showed physiological antipsychotic-like effects in wild-type but not knockout mice. These results demonstrate that can accelerate discovery.

Язык: Английский

---

Comparative Structure-Based Virtual Screening Utilizing Optimized AlphaFold Model Identifies Selective HDAC11 Inhibitor

International Journal of Molecular Sciences, Год журнала: 2024, Номер 25(2), С. 1358 - 1358

Опубликована: Янв. 22, 2024

HDAC11 is a class IV histone deacylase with no crystal structure reported so far. The catalytic domain of shares low sequence identity other HDAC isoforms, which makes conventional homology modeling less reliable. AlphaFold machine learning approach that can predict the 3D proteins high accuracy even in absence similar structures. However, fact models are predicted small molecules and ions/cofactors complicates their utilization for drug design. Previously, we optimized an model by adding zinc ion minimization presence inhibitors. In current study, implement comparative structure-based virtual screening utilizing previously to identify novel selective stepwise was successful identifying hit subsequently tested using vitro enzymatic assay. compound showed IC50 value 3.5 µM could selectively inhibit over subtypes at 10 concentration. addition, carried out molecular dynamics simulations further confirm binding hypothesis obtained docking study. These results reinforce presented optimization applicability search inhibitors discovery.

Язык: Английский

---

AmIActive (AIA): A Large-scale QSAR Based Target Fishing and Polypharmacology Predictive Web Tool

Journal of Molecular Biology, Год журнала: 2025, Номер unknown, С. 169090 - 169090

Опубликована: Март 1, 2025

Язык: Английский

---