Artificial intelligence in small molecule drug discovery from 2018 to 2023: Does it really work?

Lv, Qi; Zhou, Feilong; Liu, Xinhua; Zhi, Liping

doi:10.1016/j.bioorg.2023.106894

Cited by 15 publications

(2 citation statements)

References 72 publications

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“…The process of drug discovery needs a lot of time and efforts, [12,13] and it can be expedited with the adoption of computational tools [14] or artificial intelligence [15,16] or structure based drug design. [17] The in-silico theoretical simulations and calculations used in the field of drug design offer the excellent alternatives to the in vitro and in vivo approaches along with the reduction of attrition rates of drug candidates.…”

Section: Molecular Dynamics Simulationsmentioning

confidence: 99%

Recent Advancements and Developments of Molecular Dynamics Simulations in the Discovery of Anti‐protozoal Agents

Dhameliya,

Vekariya,

Vaddoriya

et al. 2024

ChemistrySelect

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Molecular dynamics simulations have been developed as the most powerful tool in the field of computational chemistry for the discovery of novel anti‐protozoal agents against malaria. In this review, the recent advancements and developments in the field of molecular dynamics simulations driven identification and optimization of promising anti‐protozoal agents have been highlighted along with the use of advanced simulation softwares, energy calculations, stability parameters, and many more. To the best of our knowledge, the present work has been the first review article to highlight the use of molecular dynamics simulations for the discovery of anti‐plasmodial agents with the aim to dig out the significant insights into the simulations having the applications for the benefit of computational and medicinal chemists.

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Section: Molecular Dynamics Simulationsmentioning

confidence: 99%

Recent Advancements and Developments of Molecular Dynamics Simulations in the Discovery of Anti‐protozoal Agents

Dhameliya,

Vekariya,

Vaddoriya

et al. 2024

ChemistrySelect

View full text Add to dashboard Cite

show abstract

“…With the establishment of some libraries containing structurally diverse compounds and the extraordinary advances of machine learning and deep learning, artificial intelligence (AI) has begun to participate in the screening of small molecule drugs 115 , 116 , including protein structural prediction, molecular virtual screening, molecular design, and drug pharmacokinetic prediction 117 , 118 . At present, novel small molecule inhibitors of CD47 have been predicted and designed by AI technology 36 .…”

Section: Prospectmentioning

confidence: 99%

Development of small molecule drugs targeting immune checkpoints

Chen,

Zhao,

Liu

et al. 2024

Cancer Biol Med

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Immune checkpoint inhibitors (ICIs) are used to relieve and refuel anti-tumor immunity by blocking the interaction, transcription, and translation of co-inhibitory immune checkpoints or degrading co-inhibitory immune checkpoints. Thousands of small molecule drugs or biological materials, especially antibody-based ICIs, are actively being studied and antibodies are currently widely used. Limitations, such as anti-tumor efficacy, poor membrane permeability, and unneglected tolerance issues of antibody-based ICIs, remain evident but are thought to be overcome by small molecule drugs. Recent structural studies have broadened the scope of candidate immune checkpoint molecules, as well as innovative chemical inhibitors. By way of comparison, small molecule drug-based ICIs represent superior oral bioavailability and favorable pharmacokinetic features. Several ongoing clinical trials are exploring the synergetic effect of ICIs and other therapeutic strategies based on multiple ICI functions, including immune regulation, anti-angiogenesis, and cell cycle regulation. In this review we summarized the current progression of small molecule ICIs and the mechanism underlying immune checkpoint proteins, which will lay the foundation for further exploration.

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Discovery of Antimicrobial Lysins from the “Dark Matter” of Uncharacterized Phages Using Artificial Intelligence

Zhang,

Li,

Zou

et al. 2024

Advanced Science

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The rapid rise of antibiotic resistance and slow discovery of new antibiotics have threatened global health. While novel phage lysins have emerged as potential antibacterial agents, experimental screening methods for novel lysins pose significant challenges due to the enormous workload. Here, the first unified software package, namely DeepLysin, is developed to employ artificial intelligence for mining the vast genome reservoirs (“dark matter”) for novel antibacterial phage lysins. Putative lysins are computationally screened from uncharacterized Staphylococcus aureus phages and 17 novel lysins are randomly selected for experimental validation. Seven candidates exhibit excellent in vitro antibacterial activity, with LLysSA9 exceeding that of the best‐in‐class alternative. The efficacy of LLysSA9 is further demonstrated in mouse bloodstream and wound infection models. Therefore, this study demonstrates the potential of integrating computational and experimental approaches to expedite the discovery of new antibacterial proteins for combating increasing antimicrobial resistance.

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Artificial intelligence in small molecule drug discovery from 2018 to 2023: Does it really work?

Cited by 15 publications

References 72 publications

Recent Advancements and Developments of Molecular Dynamics Simulations in the Discovery of Anti‐protozoal Agents

Recent Advancements and Developments of Molecular Dynamics Simulations in the Discovery of Anti‐protozoal Agents

Development of small molecule drugs targeting immune checkpoints

Discovery of Antimicrobial Lysins from the “Dark Matter” of Uncharacterized Phages Using Artificial Intelligence

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