INTRODUCTION
Epigenetic drug discovery remains a promising drug discovery strategy that has long been driven by advances in computational approaches. The subfield of epi-informatics, established more than a decade ago, continues to evolve rapidly as emerging machine learning methodologies reshape and expand its applications.
AREAS COVERED
The authors provide an updated overview of bioinformatics, chemoinformatics, and machine learning methodologies used to identify, design, and optimize compounds, primarily small-molecules, that modulate epigenetic processes with therapeutic potential. The discussion is based on a comprehensive literature analysis of peer-reviewed literature, encompassing 7,185 unique research articles published between 2000 up to 2025. The article also examines the epigenetic drug discovery landscape by analyzing the most extensively investigated epigenetic targets and emerging research trends.
EXPERT OPINION
Epi-informatics has evolved into a distinct interdisciplinary field integrating bioinformatics, chemoinformatics, and artificial intelligence to advance epigenetic drug discovery. Although rapid progress in multi-omics integration, molecular modeling, and generative artificial intelligence is accelerating the identification of drug candidates, future success will depend on high-quality, standardized data, interpretable machine learning models, and rigorous experimental validation that ensure reproducibility. Addressing these challenges will further advance epi-informatics in oncology research and an expanding range of complex diseases.
Aylin del Moral-Morales, Erik D. Díaz-Dionisio, José L. Medina-Franco· Expert Opinion on Drug Disco...· 0 citations
Solubility is a widely measured physicochemical property and a highly sought-after descriptor in the evaluation of molecules for drug design and development, medicinal chemistry, and agrochemical sciences. Consequently, a wide range of in silico models based on deductive and inductive reasoning have been developed. However, the General Solubility Equation (GSE) stands out as a simple, thermodynamically derived equation to predict intrinsic solubility (log S0, in mol/L) of molecules from their melting point and n-octanol/water partition coefficient (log PN). Despite its widespread use, the applicability of this equation has not been rigorously evaluated to establish when it can or cannot produce accurate predictions. Therefore, a large-scale evaluation of the GSE was conducted using an extensive database of fully experimental melting point and log PN data for 2740, mostly drug-like compounds. A systematic analysis based on cheminformatics descriptors was conducted to identify the primary factors that allow the GSE to achieve predictions within the experimental uncertainty range (±1 log S unit). This information has been used to create GSESolver: a supervised machine learning classification model that, based on the selected descriptors, can predict whether the GSE will perform accurately solely from its SMILES string. GSESolver tool can be used by anyone in the scientific community to quickly assess molecule solubility and it was developed under the FAIR principles (Findable, Accessible, Interoperable, and Reusable) in a Google Colab notebook to ensure ease of use. Finally, the utility of the GSESolver tool was exploited in medicinal chemistry applications for the in silico prediction of the Maximum Absorbable Dose (MAD) of potential drugs, and in a rational agrochemical design application where solubility is a key factor.
Esteban Bertsch-Aguilar, Nahomy Quirós, Frederick Schosinsky et al.· Journal of Chemical Informat...· 0 citations
FOXM1 is a cell proliferation-driving transcription factor activated by phosphorylation-induced conformational changes. In its inactive state, an intramolecular β-hairpin within the transactivation domain (TAD) binds the N-terminal repressor domain (NRD), forming a composite β-sheet that locks the protein in an autoinhibited conformation. Despite the known importance of this regulatory switch, the molecular events that unlock FOXM1 remain poorly characterized. Here, we performed 5 μs all-atom molecular dynamics simulations of human FOXM1b NRD-TAD complexes in both unphosphorylated and tetra-phosphorylated states, modeling four experimentally validated regulatory phosphosites. Our results showed that phosphorylation induces local unfolding of the β-hairpin beginning at Ser715, located at the hairpin turn, and propagates to global disruption of the NRD interface through hydrogen bond loss, salt bridge rupture, and secondary structure collapse. In contrast, the unphosphorylated complex maintains stable hairpin geometry and interdomain contacts. Additional replicate tetra-phosphorylated simulations and a monophosphorylated Ser715 simulation reproduced the β-hairpin unfolding event, supporting both reproducibility and the sufficiency of Ser715 phosphorylation in initiating this transition. Per-residue MM-PBSA energy decomposition further reveals that phosphorylation redistributes interdomain interaction energetics, with Ser715 emerging as the dominant locus of energetic perturbation despite the presence of multiple phosphosites. Together, these findings support a phosphorylation-triggered order-to-disorder transition that relieves FOXM1 autoinhibition and highlight Ser715 as a key structural and energetic switch. Our study provides a dynamic molecular framework for targeting FOXM1 activation via its regulatory fold.
Sara Alrawashdeh, Raghd Obidat, Aylin Del Moral-Morales et al.· Journal of Chemical Informat...· 0 citations
The rapid spread of antimicrobial resistance, particularly among pathogens such as Staphylococcus aureus and Escherichia coli, highlights the urgent need for novel antibacterial agents with new mechanisms of action. The bacterial enoyl‐acyl carrier protein reductase (FabI), an essential enzyme in fatty acid biosynthesis, represents a promising target for narrow‐spectrum antimicrobials. This study aimed to define consensus pharmacophore models and interaction profiles for FabI through an integrated computational approach. ConPhar and FTMap analyses were applied to experimental structures and molecular dynamics (MD) simulations, while protein–ligand interactions from crystallographic complexes were evaluated using PLIP. Results revealed a conserved binding core involving residues Y156/Y157 and A95 in both species. We also assessed the reliability of residues located in flexible regions by comparing MD snapshots and experimental structures, as well as the contribution of inhibitor–cofactor interactions. Surface mapping identified Y146/Y147 as a key residue, consistent with its reported role in resistance mutations. Additionally, residues I200 (E. coli), V201 (S. aureus), and F203/F204 were identified as potential unexplored interaction sites. Finally, validated consensus pharmacophore models were proposed for future virtual screening and inhibitor design.
P. T. T. F. Leite, Lucas H S Ocarino, G. Veríssimo et al.· ChemMedChem· 0 citations
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