Bioinformatics in Drug Design and Discovery

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Biomolecules biomolecules	4.8	9.4	2011	18.4 Days	CHF 2700	Submit
International Journal of Molecular Sciences ijms	4.9	8.1	2000	16.8 Days	CHF 2900	Submit
Marine Drugs marinedrugs	4.9	9.6	2003	13.7 Days	CHF 2900	Submit
Molecules molecules	4.2	7.4	1996	15.1 Days	CHF 2700	Submit
Scientia Pharmaceutica scipharm	2.3	4.6	1930	26.1 Days	CHF 1000	Submit
Genes genes	2.8	5.2	2010	14.9 Days	CHF 2600	Submit
Pharmaceutics pharmaceutics	4.9	7.9	2009	15.5 Days	CHF 2900	Submit
Crystals crystals	2.4	4.2	2011	11.1 Days	CHF 2100	Submit

14 pages, 4138 KiB

Open AccessArticle

ICVAE: Interpretable Conditional Variational Autoencoder for De Novo Molecular Design

by Xiaqiong Fan, Senlin Fang, Zhengyan Li, Hongchao Ji, Minghan Yue, Jiamin Li and Xiaozhen Ren

Int. J. Mol. Sci. 2025, 26(9), 3980; https://doi.org/10.3390/ijms26093980 - 23 Apr 2025

Abstract

Recent studies have demonstrated that machine learning-based generative models can create novel molecules with desirable properties. Among them, Conditional Variational Autoencoder (CVAE) is a powerful approach to generate molecules with desired physiochemical and pharmacological properties. However, the CVAE’s latent space is still a [...] Read more.

Recent studies have demonstrated that machine learning-based generative models can create novel molecules with desirable properties. Among them, Conditional Variational Autoencoder (CVAE) is a powerful approach to generate molecules with desired physiochemical and pharmacological properties. However, the CVAE’s latent space is still a black-box, making it difficult to understand the relationship between the latent space and molecular properties. To address this issue, we propose the Interpretable Conditional Variational Autoencoder (ICVAE), which introduces a modified loss function that correlates the latent value with molecular properties. ICVAE established a linear mapping between latent variables and molecular properties. This linearity is not only crucial for improving interpretability, by assigning clear semantic meaning to latent dimensions, but also provides a practical advantage. It enables direct manipulation of molecular attributes through simple coordinate shifts in latent space, rather than relying on opaque, black-box optimization algorithms. Our experimental results show that the ICVAE can linearly relate one or multiple molecular properties with the latent value and generate molecules with precise properties by controlling the latent values. The ICVAE’s interpretability allows us to gain insight into the molecular generation process, making it a promising approach in drug discovery and material design. Full article

(This article belongs to the Topic Bioinformatics in Drug Design and Discovery—2nd Edition)

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15 pages, 3325 KiB

Open AccessArticle

Synthesis, Crystal Structure, DFT Analysis and Docking Studies of a Novel Spiro Compound Effecting on EGR-1-Regulated Gene Expression

by Soon Young Shin, Euitaek Jung, Youngshim Lee, Ha-Jin Lee, Hyeonhwa Lee, Jinju Yoo, Seunghyun Ahn and Dongsoo Koh

Crystals 2025, 15(4), 338; https://doi.org/10.3390/cryst15040338 - 2 Apr 2025

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Abstract

The spiro compound, 5,5′-dimethoxy-1,3-bis(3-(trifluoromethyl)phenyl)-3,3a-dihydro-1H-spiro[cyclopenta[a]indene-2,2′-indene]-1′,8(3′H,8aH)-dione (4), was synthesized and identified by NMR spectroscopy, mass spectrometry, and X-ray crystallography. Compound 4, C₃₆H₂₆F₆O₄, was crystallized in the triclinic space group P-1with the cell parameters [...] Read more.

The spiro compound, 5,5′-dimethoxy-1,3-bis(3-(trifluoromethyl)phenyl)-3,3a-dihydro-1H-spiro[cyclopenta[a]indene-2,2′-indene]-1′,8(3′H,8aH)-dione (4), was synthesized and identified by NMR spectroscopy, mass spectrometry, and X-ray crystallography. Compound 4, C₃₆H₂₆F₆O₄, was crystallized in the triclinic space group P-1with the cell parameters a = 8.8669(5) Å, b = 10.5298(8) Å, c = 17.0135(11) Å, α = 91.396(2)°, β = 90.490(2)°, γ = 109.235°, V = 1499.14(17) Å³, Z = 2. In an asymmetric unit, two molecules are packed by short contacts to form an inversion dimer. The molecules are linked into chains along the a- and b-axis directions by additional short contacts in the crystal. Compound 4 was synthesized by the dimerization of (E)-5-methoxy-2-(3-(trifluoromethyl)benzylidene)-2,3-dihydro-1H-inden-1-one (3). (E)-5-Methoxy-2-(3-methoxybenzylidene)-2,3-dihydro-1H-inden-1-one (5), one of the analogs of compound 3, was compared with compound 4 based on in vitro experiments, DFT calculations, and an in silico docking study. The HOMO/LUMO energy difference and binding energy difference between the two compounds are consistent with the results obtained from an in vitro assay where 4 showed a better effect than 5. To evaluate the biological activity of 4, we examined its inhibitory effects on Early Growth Respone-1 (EGR-1)-regulated gene expression in HaCaT keratinocytes. Treatment of cells with 4 reduced interleukin-4 (IL-4)-induced thymic stromal lymphopoietin (TSLP) mRNA levels, as revealed by reverse transcription-polymerase chain reaction and quantitative real-time PCR. Furthermore, the electrophoretic mobility shift assay demonstrated that 4 inhibited IL-4-induced DNA binding of EGR-1 to the promoter region of the TSLP gene. Full article

(This article belongs to the Topic Bioinformatics in Drug Design and Discovery—2nd Edition)

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18 pages, 11514 KiB

Open AccessArticle

Exploring Cannabidiol’s Therapeutic Role in Colorectal Cancer: Network Pharmacology and Molecular Docking Insights

by Juan Manuel Guzmán-Flores, Fernando Martínez-Esquivias, Antistio Alviz-Amador, Guadalupe Thonanzyn Avilés-Rodríguez and Michel Fabricio García-Azuela

Sci. Pharm. 2025, 93(1), 12; https://doi.org/10.3390/scipharm93010012 - 28 Feb 2025

Viewed by 1448

Abstract

Background: Colorectal cancer (CRC) is one of the most prevalent cancers worldwide, and current treatments have significant side effects. Cannabidiol (CBD), a compound derived from Cannabis sativa, has demonstrated promising anticancer properties. However, further investigation is required to elucidate its underlying molecular [...] Read more.

Background: Colorectal cancer (CRC) is one of the most prevalent cancers worldwide, and current treatments have significant side effects. Cannabidiol (CBD), a compound derived from Cannabis sativa, has demonstrated promising anticancer properties. However, further investigation is required to elucidate its underlying molecular mechanisms. Methods: Network pharmacology and molecular docking analysis approaches were utilized. Molecular targets of CBD and CRC-associated genes were identified using the Swiss Target Prediction, Malacards, and DisGeNet databases. Protein–protein interactions were analyzed using the STRING and Cytoscape. Ontology enrichment was conducted using ShinyGO, and gene expression and immune infiltration were evaluated with UALCAN and TISIDB. Results: We found 95 common genes between CRC and CBD targets. Six major genes (ANXA5, IGF1R, JAK2, MAPK8, MDM2, and PARP1) were particularly interesting due to their high connectivity and role in relevant metabolic pathways. The results of the molecular docking analysis indicated that CBD interacts favorably with these genes, modulating critical pathways such as RAS/MAPK and PI3K-AKT/FoxO, which are involved in cell proliferation, apoptosis, and cell cycle regulation. ANXA5 and JAK2 were identified as particularly relevant, as they correlated significantly with immune cell infiltration, suggesting a role in the immunoregulation of the tumor microenvironment. Conclusions: CBD has the potential to modulate key molecular processes in CRC through specific pathways and core genes, presenting itself as a possible complementary therapy to improve efficacy and reduce the adverse effects of conventional treatments. Full article

(This article belongs to the Topic Bioinformatics in Drug Design and Discovery—2nd Edition)

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16 pages, 2644 KiB

Open AccessCommunication

A Virtual Screening Approach to Evaluate the Multitarget Potential of a Chalcone Library with Binding Properties to Oligopeptidase B and Cysteine Proteinase B from Leishmania (Viannia) braziliensis

by Patrícia Queiroz Monteiro, Edgar Schaeffer, Alcides José Monteiro da Silva, Carlos Roberto Alves and Franklin Souza-Silva

Int. J. Mol. Sci. 2025, 26(5), 2025; https://doi.org/10.3390/ijms26052025 - 26 Feb 2025

Viewed by 428

Abstract

Leishmaniasis remains a significant public health problem in Brazil, particularly due to Leishmania (Viannia) braziliensis, which is associated with severe dermatological syndromes. The current treatments are limited by toxicity and uncertain efficacy, highlighting the need for new compounds with pharmacological potential. This [...] Read more.

Leishmaniasis remains a significant public health problem in Brazil, particularly due to Leishmania (Viannia) braziliensis, which is associated with severe dermatological syndromes. The current treatments are limited by toxicity and uncertain efficacy, highlighting the need for new compounds with pharmacological potential. This study investigates chalcones as multitarget binding agents for oligopeptidase B (OPB) and cysteine proteinase B (CPB), which are critical pathogenic determinants of L. (V.) braziliensis. The methodology involved replacing methoxy groups with aryl motifs at various positions within the chalcone structures and introducing specific functional groups at the C-4 position. This was followed by a virtual screening approach using molecular docking to assess interactions with the target proteinases. Several chalcones from the virtual library (n = 178) exhibited high binding affinities for OPB and CPB, outperforming control ligands. A total of 30 chalcones with multitarget potential were identified, with fluorinated compounds C-191 and C-135 emerging as promising inhibitors, distinguished by the best energy rankings for both enzymes. ADMET analyses confirmed the viability of these chalcones as drug candidates, with most adhering to Lipinski’s rules. These data suggest that chalcones may provide new multitarget treatment options for leishmaniasis. Full article

(This article belongs to the Topic Bioinformatics in Drug Design and Discovery—2nd Edition)

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13 pages, 6200 KiB

Open AccessArticle

Discovery of Novel Pyridin-2-yl Urea Inhibitors Targeting ASK1 Kinase and Its Binding Mode by Absolute Protein–Ligand Binding Free Energy Calculations

by Lingzhi Wang, Yalei Gao, Yuying Chen, Zhenzhou Tang, Xiao Lin, Meng Bai, Pei Cao and Kai Liu

Int. J. Mol. Sci. 2025, 26(4), 1527; https://doi.org/10.3390/ijms26041527 - 12 Feb 2025

Viewed by 848

Abstract

Apoptosis signal-regulating kinase 1 (ASK1), a key component of the mitogen-activated protein kinase (MAPK) cascades, has been identified as a promising therapeutic target owing to its critical role in signal transduction pathways. In this study, we proposed novel pyridin-2-yl urea inhibitors exhibiting favorable [...] Read more.

Apoptosis signal-regulating kinase 1 (ASK1), a key component of the mitogen-activated protein kinase (MAPK) cascades, has been identified as a promising therapeutic target owing to its critical role in signal transduction pathways. In this study, we proposed novel pyridin-2-yl urea inhibitors exhibiting favorable physicochemical properties. The potency of these compounds was validated through in vitro protein bioassays. The inhibition (IC₅₀) of compound 2 was 1.55 ± 0.27 nM, which was comparable to the known clinical inhibitor, Selonsertib. To further optimize the hit compounds, two possible binding modes were initially predicted by molecular docking. Absolute binding free energy (BFE) calculations based on molecular dynamics simulations further discriminated the binding modes, presenting good tendency with bioassay results. This strategy, underpinned by BFE calculations, has the great potential to expedite the drug discovery process in the targeting of ASK1 kinase. Full article

(This article belongs to the Topic Bioinformatics in Drug Design and Discovery—2nd Edition)

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19 pages, 3819 KiB

Open AccessArticle

Identification of Prospective Ebola Virus VP35 and VP40 Protein Inhibitors from Myxobacterial Natural Products

by Muhammad Hayat, Tian Gao, Ying Cao, Muhammad Rafiq, Li Zhuo and Yue-Zhong Li

Biomolecules 2024, 14(6), 660; https://doi.org/10.3390/biom14060660 - 5 Jun 2024

Cited by 4 | Viewed by 2201

Abstract

The Ebola virus (EBOV) is a lethal pathogen causing hemorrhagic fever syndrome which remains a global health challenge. In the EBOV, two multifunctional proteins, VP35 and VP40, have significant roles in replication, virion assembly, and budding from the cell and have been identified [...] Read more.

The Ebola virus (EBOV) is a lethal pathogen causing hemorrhagic fever syndrome which remains a global health challenge. In the EBOV, two multifunctional proteins, VP35 and VP40, have significant roles in replication, virion assembly, and budding from the cell and have been identified as druggable targets. In this study, we employed in silico methods comprising molecular docking, molecular dynamic simulations, and pharmacological properties to identify prospective drugs for inhibiting VP35 and VP40 proteins from the myxobacterial bioactive natural product repertoire. Cystobactamid 934-2, Cystobactamid 919-1, and Cittilin A bound firmly to VP35. Meanwhile, 2-Hydroxysorangiadenosine, Enhypyrazinone B, and Sorangiadenosine showed strong binding to the matrix protein VP40. Molecular dynamic simulations revealed that, among these compounds, Cystobactamid 919-1 and 2-Hydroxysorangiadenosine had stable interactions with their respective targets. Similarly, molecular mechanics Poisson–Boltzmann surface area (MMPBSA) calculations indicated close-fitting receptor binding with VP35 or VP40. These two compounds also exhibited good pharmacological properties. In conclusion, we identified Cystobactamid 919-1 and 2-Hydroxysorangiadenosine as potential ligands for EBOV that target VP35 and VP40 proteins. These findings signify an essential step in vitro and in vivo to validate their potential for EBOV inhibition. Full article

(This article belongs to the Topic Bioinformatics in Drug Design and Discovery—2nd Edition)

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17 pages, 2436 KiB

Open AccessArticle

An Approach for Engineering Peptides for Competitive Inhibition of the SARS-COV-2 Spike Protein

by Ana Paula de Abreu, Frederico Chaves Carvalho, Diego Mariano, Luana Luiza Bastos, Juliana Rodrigues Pereira Silva, Leandro Morais de Oliveira, Raquel C. de Melo-Minardi and Adriano de Paula Sabino

Molecules 2024, 29(7), 1577; https://doi.org/10.3390/molecules29071577 - 1 Apr 2024

Cited by 2 | Viewed by 1914

Abstract

SARS-CoV-2 is the virus responsible for a respiratory disease called COVID-19 that devastated global public health. Since 2020, there has been an intense effort by the scientific community to develop safe and effective prophylactic and therapeutic agents against this disease. In this context, [...] Read more.

SARS-CoV-2 is the virus responsible for a respiratory disease called COVID-19 that devastated global public health. Since 2020, there has been an intense effort by the scientific community to develop safe and effective prophylactic and therapeutic agents against this disease. In this context, peptides have emerged as an alternative for inhibiting the causative agent. However, designing peptides that bind efficiently is still an open challenge. Here, we show an algorithm for peptide engineering. Our strategy consists of starting with a peptide whose structure is similar to the interaction region of the human ACE2 protein with the SPIKE protein, which is important for SARS-COV-2 infection. Our methodology is based on a genetic algorithm performing systematic steps of random mutation, protein–peptide docking (using the PyRosetta library) and selecting the best-optimized peptides based on the contacts made at the peptide–protein interface. We performed three case studies to evaluate the tool parameters and compared our results with proposals presented in the literature. Additionally, we performed molecular dynamics (MD) simulations (three systems, 200 ns each) to probe whether our suggested peptides could interact with the spike protein. Our results suggest that our methodology could be a good strategy for designing peptides. Full article

(This article belongs to the Topic Bioinformatics in Drug Design and Discovery—2nd Edition)

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13 pages, 2456 KiB

Open AccessArticle

Semaglutide as a Possible Calmodulin Binder: Ligand-Based Computational Analyses and Relevance to Its Associated Reward and Appetitive Behaviour Actions

by Giuseppe Floresta, Davide Arillotta, Valeria Catalani, Gabriele Duccio Papanti Pelletier, John Martin Corkery, Amira Guirguis and Fabrizio Schifano

Sci. Pharm. 2024, 92(2), 17; https://doi.org/10.3390/scipharm92020017 - 22 Mar 2024

Cited by 1 | Viewed by 3474

Abstract

Semaglutide, a glucagon-like peptide-1 (GLP-1) receptor agonist, has gained considerable attention as a therapeutic agent for type 2 diabetes mellitus and obesity. Despite its clinical success, the precise mechanisms underlying its pharmacological effects remain incompletely understood. In this study, we employed ligand-based drug [...] Read more.

Semaglutide, a glucagon-like peptide-1 (GLP-1) receptor agonist, has gained considerable attention as a therapeutic agent for type 2 diabetes mellitus and obesity. Despite its clinical success, the precise mechanisms underlying its pharmacological effects remain incompletely understood. In this study, we employed ligand-based drug design strategies to investigate potential off-target interactions of semaglutide. Through a comprehensive in silico screening of semaglutide’s structural properties against a diverse panel of proteins, we have identified calmodulin (CaM) as a putative novel target of semaglutide. Molecular docking simulations revealed a strong interaction between semaglutide and CaM, characterized by favourable binding energies and a stable binding pose. Further molecular dynamics simulations confirmed the stability of the semaglutide–CaM complex, emphasizing the potential for a physiologically relevant interaction. In conclusion, our ligand-based drug design approach has uncovered calmodulin as a potential novel target of semaglutide. This discovery sheds light on the complex pharmacological profile of semaglutide and offers a promising direction for further research into the development of innovative therapeutic strategies for metabolic disorders. The CaM, and especially so the CaMKII, system is central in the experience of both drug- and natural-related reward. It is here hypothesized that, due to semaglutide binding, the reward pathway-based calmodulin system may be activated, and/or differently regulated. This may result in the positive semaglutide action on appetitive behaviour. Further studies are required to confirm these findings. Full article

(This article belongs to the Topic Bioinformatics in Drug Design and Discovery—2nd Edition)

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16 pages, 1005 KiB

Open AccessArticle

CONSMI: Contrastive Learning in the Simplified Molecular Input Line Entry System Helps Generate Better Molecules

by Ying Qian, Minghua Shi and Qian Zhang

Molecules 2024, 29(2), 495; https://doi.org/10.3390/molecules29020495 - 19 Jan 2024

Cited by 1 | Viewed by 2430

Abstract

In recent years, the application of deep learning in molecular de novo design has gained significant attention. One successful approach involves using SMILES representations of molecules and treating the generation task as a text generation problem, yielding promising results. However, the generation of [...] Read more.

In recent years, the application of deep learning in molecular de novo design has gained significant attention. One successful approach involves using SMILES representations of molecules and treating the generation task as a text generation problem, yielding promising results. However, the generation of more effective and novel molecules remains a key research area. Due to the fact that a molecule can have multiple SMILES representations, it is not sufficient to consider only one of them for molecular generation. To make up for this deficiency, and also motivated by the advancements in contrastive learning in natural language processing, we propose a contrastive learning framework called CONSMI to learn more comprehensive SMILES representations. This framework leverages different SMILES representations of the same molecule as positive examples and other SMILES representations as negative examples for contrastive learning. The experimental results of generation tasks demonstrate that CONSMI significantly enhances the novelty of generated molecules while maintaining a high validity. Moreover, the generated molecules have similar chemical properties compared to the original dataset. Additionally, we find that CONSMI can achieve favorable results in classifier tasks, such as the compound–protein interaction task. Full article

(This article belongs to the Topic Bioinformatics in Drug Design and Discovery—2nd Edition)

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