Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) prediction plays a central role in modern computer-aided drug discovery by enabling early evaluation of pharmacokinetic behaviour and safety liabilities. This tutorial presents a reproducible computational workflow for large-scale ADMET profiling using ADMET-AI 2.0, a graph neural network-based prediction platform, applied to 200 cyclin-dependent kinase 2 (CDK2) inhibitors curated from ChEMBL release 35. Implemented within an annotated Jupyter notebook, the workflow integrates cheminformatics preprocessing, physicochemical property analysis, drug-likeness evaluation, structural alert screening, absorption and distribution profiling, CYP450-mediated metabolism prediction, excretion assessment, toxicity evaluation, Tox21 pathway analysis, DrugBank percentile benchmarking, and composite multi-parameter optimisation scoring. A total of 104 ADMET-related outputs were generated per compound, including 52 predictive endpoints and 52 corresponding DrugBank approved-drug percentile rankings. The 200 curated CDK2 inhibitors exhibited 100
Drug discovery remains a costly and time-consuming process, motivating the development of artificial intelligence approaches to accelerate molecular design. Many generative models, including generative pre-trained transformers (GPT), rely on one-dimensional SMILES representations that lack explicit structural topology and are constrained by rigid syntax rules. Moreover, most existing models perform unconditional molecule generation, while the few conditional diffusion approaches often rely on abstract parameters rather than pharmacologically interpretable constraints. Consequently, current methods have limited ability to enforce explicit drug-like and pharmacophore requirements during molecular generation. Here, we propose GCMDiff, a multi-condition guided diffusion framework for molecular graph generation that supports controllable and interpretable molecular design. Unlike SMILES-based models, GCMDiff employs an atom-pair and bond-centric molecular graph representation that directly captures explicit chemical topologies. Crucially, we introduce an interpretable pharmacological condition embedding that integrates multi-modal constraints, including atom number, Lipinski’s Rule of Five constraints, and structural moieties derived from Checkmol, PubChem, and Rings in Drugs descriptors. These explicit features guide the diffusion process toward promising chemical spaces satisfying user-defined prerequisites. Benchmark experiments demonstrate that GCMDiff generates chemically valid and structurally diverse molecules, achieving 99.6 GCMDiff advances molecular design by employing a graph-based diffusion framework that bypasses SMILES-related syntax limitations. Its core innovation is a multi-modal condition encoder integrating drug-like properties and pharmacophores, enabling interpretable, pharmacophore-fragment-conditioned molecular generation. The model achieves 99.6
Public tumor-homing peptide (THP) resources have enabled predictors such as THPep, SCMTHP, StackTHPred, StackTHP and LLM4THP. At the same time, broader peptide machine-learning resources, including the recent PepBenchmark/PepBenchData initiative, are moving the field toward standardized datasets, preprocessing, and leaderboards across peptide-property tasks. These developments have improved comparability but have not removed task-specific problems in THP modelling: available THP benchmarks are motif-rich, chemically redundant, and susceptible to optimistic evaluation under near-neighbor overlap and potential leakage. A cheminformatics evaluation of THP predictions therefore needs to ask not only whether a peptide receives a high score, but also whether that score is independent, motif-driven, chemically redundant, hard-negative-sensitive, or near-duplicate-dependent. We present THP-NanoTarget, a reproducible peptide-cheminformatics framework for leakage-aware THP benchmarking, motif-rule comparisons, Morgan/ECFP chemical-space auditing, hard-negative evaluations, strict external-recognition filtering, and conservative analog prioritization. The primary benchmark contained 4202 peptides: 1401 THP positives and 2801 primary negatives. Under standard stratified out-of-fold evaluations, a term frequency-inverse document frequency (TF-IDF) 2–4-mer logistic-regression model achieved an area under the receiver operating characteristic curve (AUROC) of 0.784 and an area under the precision recall curve (AUPRC) of 0.716, improving over a transparent motif-rule baseline (AUROC 0.644; AUPRC 0.488). Cluster-aware validation was more conservative, with an AUROC of 0.708 and an AUPRC of 0.597 for the reference k-mer model and an AUROC of 0.715 and AUPRC of 0.612 for the best rank-averaged ensemble. Chemical-neighbor exclusion revealed local-neighborhood dependence: the AUROC declined to 0.721 at a Tanimoto of < 0.80 and 0.623 at < 0.70, while the 0.60 threshold was not evaluable. A benchmark redundancy analysis showed a median nearest-neighbor Morgan/extended connectivity fingerprint (ECFP) Tanimoto similarity of 0.837, and 95.8
The effective prioritization of protein variants affecting protein folding and interactions is critical for accurately identifying significant mutations and guiding targeted experimental validation. However, current approaches involve time-consuming manual analyses, with inconsistencies in reproducibility and limited integration of various structural datasets. This highlights the need for an automated, scalable workflow capable of efficiently analysing mutations/variants and their potential effects on protein structure and interactions. To address this, a modular bioinformatics workflow, called Automated Mutation Introduction and Analysis (AMIA), is introduced, integrating various structural analysis tools into a simplified workflow. The pipeline is organized into discrete stages as follows: mutation introduction into protein structures, polar interaction changes, protein fold energy analyses, as well as downstream molecular dynamics simulation analysis, reducing the need for constant user input and output management. In addition, allosteric site determination and small-molecule docking are also optionally available but not part of the standard workflow. Furthermore, performance bottlenecks in large protein datasets are addressed by automating data transformations and parallelizing computations, while workflow consistency and reproducibility are maintained through standardized input/output formats and automated logging. This open-source pipeline automates the visualization of mutation effects on protein structure and dynamic states, aiding in prioritizing variants for experimental validation. It allows researchers to efficiently prioritize specific mutations of mutated systems for experimental validation, reducing the need for manual input and thereby delivering reproducible and scalable insights into the impact of mutations on biological systems. AMIA framework and scripts are freely available at: https://github.com/kbrown3687524/amia for download and installation on Linux and Windows operating system. Scientific contribution The pipeline scientifically contributes to research by integrating static and dynamic structural analyses and provides a comprehensive assessment of mutation effects. It also addresses limitations in manual and fragmented workflows, delivering a reproducible and scalable framework for analysing the structural and dynamic effects of protein variants, with downstream applications in areas such as drug resistance.
Public bioactivity databases aggregate measurements from heterogeneous experimental assays, introducing systematic variability that can degrade model performance and distort uncertainty estimates. We present proteochemometric assay-aware modeling (PCAM), an extension of proteochemometrics that integrates assay descriptors alongside compound and protein features to capture assay context. Using a curated ChEMBL-derived benchmark spanning 167 G protein-coupled receptor targets, 77,081 compounds, and 5,161 assays, we compare models that omit assay information to models using either categorical assay identifiers or text-derived embeddings from assay descriptions. We evaluate generalization under three regimes of increasing distribution shift (random, scaffold-based, and assay-held-out splits) and assess both point accuracy and probabilistic quality. Overall difficulty is dominated by the split strategy, with assay-held-out evaluation inducing the strongest distribution shift. When assay identifiers are shared between training and evaluation, categorical assay descriptors provide the most consistent gains in accuracy and probabilistic scoring relative to models without assay context. Under assay-held-out evaluation, differences between assay representations contract, while text-derived embeddings show a consistent but typically small advantage. This regime corresponds to practical assay-transfer scenarios, such as protocol or vendor changes, new readouts, or sparse early measurements for a newly introduced assay, where leveraging assay text/metadata can help transfer signal from related assays. In addition, assay representations can reduce reliance on brittle cross-assay normalization in heterogeneous public datasets by allowing the model to learn assay-dependent shifts directly; however, we observed optimization instability for one configuration (a probabilistic neural network using BioBERT embeddings under the random split). We introduce an assay-aware extension of proteochemometrics that explicitly models assay context and evaluate it under increasing distribution shift. We clarify when simple assay identifiers suffice and when text-derived assay representations better support transfer to unseen assay protocols while maintaining uncertainty-aware evaluation.
