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3 pages, 168 KiB

Open AccessEditorial

Advances in Drug Design and Development for Human Therapeutics Using Artificial Intelligence-II

by Dongqing Wei, Gilles H. Peslherbe, Gurudeeban Selvaraj and Yanjing Wang

Biomolecules 2023, 13(12), 1735; https://doi.org/10.3390/biom13121735 - 2 Dec 2023

Cited by 2 | Viewed by 1719

Abstract

Building on our 2021–2022 Special Issue, “Advances in Drug Design and Development for Human Therapeutics Using Artificial Intelligence [...] Full article

(This article belongs to the Special Issue Advances in Drug Design and Development for Human Therapeutics Using Artificial Intelligence II)

Research

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13 pages, 3604 KiB

Open AccessArticle

In Silico Screening and Optimization of Cell-Penetrating Peptides Using Deep Learning Methods

by Hyejin Park, Jung-Hyun Park, Min Seok Kim, Kwangmin Cho and Jae-Min Shin

Biomolecules 2023, 13(3), 522; https://doi.org/10.3390/biom13030522 - 13 Mar 2023

Cited by 9 | Viewed by 3312

Abstract

Cell-penetrating peptides (CPPs) have great potential to deliver bioactive agents into cells. Although there have been many recent advances in CPP-related research, it is still important to develop more efficient CPPs. The development of CPPs by in silico methods is a very useful [...] Read more.

Cell-penetrating peptides (CPPs) have great potential to deliver bioactive agents into cells. Although there have been many recent advances in CPP-related research, it is still important to develop more efficient CPPs. The development of CPPs by in silico methods is a very useful addition to experimental methods, but in many cases it can lead to a large number of false-positive results. In this study, we developed a deep-learning-based CPP prediction method, AiCPP, to develop novel CPPs. AiCPP uses a large number of peptide sequences derived from human-reference proteins as a negative set to reduce false-positive predictions and adopts a method to learn small-length peptide sequence motifs that may have CPP tendencies. Using AiCPP, we found that short peptide sequences derived from amyloid precursor proteins are efficient new CPPs, and experimentally confirmed that these CPP sequences can be further optimized. Full article

(This article belongs to the Special Issue Advances in Drug Design and Development for Human Therapeutics Using Artificial Intelligence II)

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13 pages, 3375 KiB

Open AccessEditor’s ChoiceArticle

Protein Design Using Physics Informed Neural Networks

by Sara Ibrahim Omar, Chen Keasar, Ariel J. Ben-Sasson and Eldad Haber

Biomolecules 2023, 13(3), 457; https://doi.org/10.3390/biom13030457 - 1 Mar 2023

Cited by 4 | Viewed by 3514

Abstract

The inverse protein folding problem, also known as protein sequence design, seeks to predict an amino acid sequence that folds into a specific structure and performs a specific function. Recent advancements in machine learning techniques have been successful in generating functional sequences, outperforming [...] Read more.

The inverse protein folding problem, also known as protein sequence design, seeks to predict an amino acid sequence that folds into a specific structure and performs a specific function. Recent advancements in machine learning techniques have been successful in generating functional sequences, outperforming previous energy function-based methods. However, these machine learning methods are limited in their interoperability and robustness, especially when designing proteins that must function under non-ambient conditions, such as high temperature, extreme pH, or in various ionic solvents. To address this issue, we propose a new Physics-Informed Neural Networks (PINNs)-based protein sequence design approach. Our approach combines all-atom molecular dynamics simulations, a PINNs MD surrogate model, and a relaxation of binary programming to solve the protein design task while optimizing both energy and the structural stability of proteins. We demonstrate the effectiveness of our design framework in designing proteins that can function under non-ambient conditions. Full article

(This article belongs to the Special Issue Advances in Drug Design and Development for Human Therapeutics Using Artificial Intelligence II)

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10 pages, 515 KiB

Open AccessFeature PaperArticle

Application of an Artificial Neural Network to Identify the Factors Influencing Neurorehabilitation Outcomes of Patients with Ischemic Stroke Treated with Thrombolysis

by Marco Iosa, Stefano Paolucci, Gabriella Antonucci, Irene Ciancarelli and Giovanni Morone

Biomolecules 2023, 13(2), 334; https://doi.org/10.3390/biom13020334 - 9 Feb 2023

Cited by 5 | Viewed by 2119

Abstract

The administration of thrombolysis usually reduces the risk of death and the consequences of stroke in the acute phase. However, having received thrombolysis administration is not a prognostic factor for neurorehabilitation outcome in the subacute phase of stroke. It is conceivably due to [...] Read more.

The administration of thrombolysis usually reduces the risk of death and the consequences of stroke in the acute phase. However, having received thrombolysis administration is not a prognostic factor for neurorehabilitation outcome in the subacute phase of stroke. It is conceivably due to the complex intertwining of many clinical factors. An artificial neural network (ANN) analysis could be helpful in identifying the prognostic factors of neurorehabilitation outcomes and assigning a weight to each of the factors considered. This study hypothesizes that the prognostic factors could be different between patients who received and those who did not receive thrombolytic treatment, even if thrombolysis is not a prognostic factor per se. In a sample of 862 patients with ischemic stroke, the tested ANN identified some common factors (such as disability at admission, age, unilateral spatial neglect), some factors with higher weight in patients who received thrombolysis (hypertension, epilepsy, aphasia, obesity), and some other factors with higher weight in the other patients (dysphagia, malnutrition, total arterial circulatory infarction). Despite the fact that thrombolysis is not an independent prognostic factor for neurorehabilitation, it seems to modify the relative importance of other clinical factors in predicting which patients will better respond to neurorehabilitation. Full article

(This article belongs to the Special Issue Advances in Drug Design and Development for Human Therapeutics Using Artificial Intelligence II)

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21 pages, 6441 KiB

Open AccessArticle

Identification of Activated Cdc42-Associated Kinase Inhibitors as Potential Anticancer Agents Using Pharmacoinformatic Approaches

by Vikas Kumar, Raj Kumar, Shraddha Parate, Danishuddin, Gihwan Lee, Moonhyuk Kwon, Seong-Hee Jeong, Hyeon-Su Ro, Keun Woo Lee and Seon-Won Kim

Biomolecules 2023, 13(2), 217; https://doi.org/10.3390/biom13020217 - 22 Jan 2023

Cited by 4 | Viewed by 3489

Abstract

Background: Activated Cdc42-associated kinase (ACK1) is essential for numerous cellular functions, such as growth, proliferation, and migration. ACK1 signaling occurs through multiple receptor tyrosine kinases; therefore, its inhibition can provide effective antiproliferative effects against multiple human cancers. A number of ACK1-specific inhibitors were [...] Read more.

Background: Activated Cdc42-associated kinase (ACK1) is essential for numerous cellular functions, such as growth, proliferation, and migration. ACK1 signaling occurs through multiple receptor tyrosine kinases; therefore, its inhibition can provide effective antiproliferative effects against multiple human cancers. A number of ACK1-specific inhibitors were designed and discovered in the previous decade, but none have reached the clinic. Potent and selective ACK1 inhibitors are urgently needed. Methods: In the present investigation, the pharmacophore model (PM) was rationally built utilizing two distinct inhibitors coupled with ACK1 crystal structures. The generated PM was utilized to screen the drug-like database generated from the four chemical databases. The binding mode of pharmacophore-mapped compounds was predicted using a molecular docking (MD) study. The selected hit-protein complexes from MD were studied under all-atom molecular dynamics simulations (MDS) for 500 ns. The obtained trajectories were ranked using binding free energy calculations (ΔG kJ/mol) and Gibb’s free energy landscape. Results: Our results indicate that the three hit compounds displayed higher binding affinity toward ACK1 when compared with the known multi-kinase inhibitor dasatinib. The inter-molecular interactions of Hit1 and Hit3 reveal that compounds form desirable hydrogen bond interactions with gatekeeper T205, hinge region A208, and DFG motif D270. As a result, we anticipate that the proposed scaffolds might help in the design of promising selective ACK1 inhibitors. Full article

(This article belongs to the Special Issue Advances in Drug Design and Development for Human Therapeutics Using Artificial Intelligence II)

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16 pages, 3762 KiB

Open AccessArticle

miDruglikeness: Subdivisional Drug-Likeness Prediction Models Using Active Ensemble Learning Strategies

by Chenjing Cai, Haoyu Lin, Hongyi Wang, Youjun Xu, Qi Ouyang, Luhua Lai and Jianfeng Pei

Biomolecules 2023, 13(1), 29; https://doi.org/10.3390/biom13010029 - 23 Dec 2022

Cited by 15 | Viewed by 3187

Abstract

The drug development pipeline involves several stages including in vitro assays, in vivo assays, and clinical trials. For candidate selection, it is important to consider that a compound will successfully pass through these stages. Using graph neural networks, we developed three subdivisional models [...] Read more.

The drug development pipeline involves several stages including in vitro assays, in vivo assays, and clinical trials. For candidate selection, it is important to consider that a compound will successfully pass through these stages. Using graph neural networks, we developed three subdivisional models to individually predict the capacity of a compound to enter in vivo testing, clinical trials, and market approval stages. Furthermore, we proposed a strategy combing both active learning and ensemble learning to improve the quality of the models. The models achieved satisfactory performance in the internal test datasets and four self-collected external test datasets. We also employed the models as a general index to make an evaluation on a widely known benchmark dataset DEKOIS 2.0, and surprisingly found a powerful ability on virtual screening tasks. Our model system (termed as miDruglikeness) provides a comprehensive drug-likeness prediction tool for drug discovery and development. Full article

(This article belongs to the Special Issue Advances in Drug Design and Development for Human Therapeutics Using Artificial Intelligence II)

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34 pages, 5287 KiB

Open AccessArticle

Identification of Novel Ribonucleotide Reductase Inhibitors for Therapeutic Application in Bile Tract Cancer: An Advanced Pharmacoinformatics Study

by Md Ataul Islam, Mayuri Makarand Barshetty, Sridhar Srinivasan, Dawood Babu Dudekula, V. P. Subramanyam Rallabandi, Sameer Mohammed, Sathishkumar Natarajan and Junhyung Park

Biomolecules 2022, 12(9), 1279; https://doi.org/10.3390/biom12091279 - 10 Sep 2022

Cited by 1 | Viewed by 2483

Abstract

Biliary tract cancer (BTC) is constituted by a heterogeneous group of malignant tumors that may develop in the biliary tract, and it is the second most common liver cancer. Human ribonucleotide reductase M1 (hRRM1) has already been proven to be a potential BTC [...] Read more.

Biliary tract cancer (BTC) is constituted by a heterogeneous group of malignant tumors that may develop in the biliary tract, and it is the second most common liver cancer. Human ribonucleotide reductase M1 (hRRM1) has already been proven to be a potential BTC target. In the current study, a de novo design approach was used to generate novel and effective chemical therapeutics for BTC. A set of comprehensive pharmacoinformatics approaches was implemented and, finally, seventeen potential molecules were found to be effective for the modulation of hRRM1 activity. Molecular docking, negative image-based ShaEP scoring, absolute binding free energy, in silico pharmacokinetics, and toxicity assessments corroborated the potentiality of the selected molecules. Almost all molecules showed higher affinity in comparison to gemcitabine and naphthyl salicylic acyl hydrazone (NSAH). On binding interaction analysis, a number of critical amino acids was found to hold the molecules at the active site cavity. The molecular dynamics (MD) simulation study also indicated the stability between protein and ligands. High negative MM-GBSA (molecular mechanics generalized Born and surface area) binding free energy indicated the potentiality of the molecules. Therefore, the proposed molecules might have the potential to be effective therapeutics for the management of BTC. Full article

(This article belongs to the Special Issue Advances in Drug Design and Development for Human Therapeutics Using Artificial Intelligence II)

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10 pages, 2204 KiB

Open AccessArticle

Prediction of Potential Commercially Available Inhibitors against SARS-CoV-2 by Multi-Task Deep Learning Model

by Fan Hu, Jiaxin Jiang and Peng Yin

Biomolecules 2022, 12(8), 1156; https://doi.org/10.3390/biom12081156 - 21 Aug 2022

Cited by 38 | Viewed by 2535

Abstract

The outbreak of COVID-19 caused millions of deaths worldwide, and the number of total infections is still rising. It is necessary to identify some potentially effective drugs that can be used to prevent the development of severe symptoms, or even death for those [...] Read more.

The outbreak of COVID-19 caused millions of deaths worldwide, and the number of total infections is still rising. It is necessary to identify some potentially effective drugs that can be used to prevent the development of severe symptoms, or even death for those infected. Fortunately, many efforts have been made and several effective drugs have been identified. The rapidly increasing amount of data is of great help for training an effective and specific deep learning model. In this study, we propose a multi-task deep learning model for the purpose of screening commercially available and effective inhibitors against SARS-CoV-2. First, we pretrained a model on several heterogenous protein–ligand interaction datasets. The model achieved competitive results on some benchmark datasets. Next, a coronavirus-specific dataset was collected and used to fine-tune the model. Then, the fine-tuned model was used to select commercially available drugs against SARS-CoV-2 protein targets. Overall, twenty compounds were listed as potential inhibitors. We further explored the model interpretability and exhibited the predicted important binding sites. Based on this prediction, molecular docking was also performed to visualize the binding modes of the selected inhibitors. Full article

(This article belongs to the Special Issue Advances in Drug Design and Development for Human Therapeutics Using Artificial Intelligence II)

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