Computational Design Points to New Indolinone Drug Candidates Against Cancer

A team of computational chemists and pharmaceutical researchers from Morocco, India, and the United Arab Emirates has unveiled a new pipeline for designing potential anticancer drugs entirely on computers before a single molecule is ever made in a laboratory. Working from a family of compounds known as indolinone derivatives, the researchers built a statistical model that links molecular structure to anticancer potency, used it to blueprint ten brand-new molecules, and then subjected the two most promising designs to a battery of simulations. Their findings, published in Frontiers in Bioinformatics, identify two virtual candidates, dubbed Pred 9 and Pred 10, that now await the ultimate test: real-world synthesis and biological evaluation.

The biological target at the heart of the study is the RAF–MEK–ERK signaling pathway, often called the MAPK pathway, a molecular relay system that governs cell growth, differentiation, and survival. When mutations strike key proteins such as RAS or BRAF, this pathway can become permanently switched on, driving the uncontrolled proliferation that defines cancer. Lung cancer is particularly devastating in this regard: it was the leading cause of cancer-related death worldwide in 2022, claiming roughly 1.8 million lives and accounting for 18.7 percent of all cancer deaths. Existing drugs that target this pathway, including the BRAF inhibitors vemurafenib and dabrafenib and the MEK inhibitors trametinib and selumetinib, can slow tumor growth, but resistance almost invariably emerges, often within six to eight months of treatment. This relentless problem motivates the search for new chemical entities that can attack the pathway at its most vulnerable junctions.

The researchers chose MEK1, the kinase sitting between RAF and ERK in the cascade, as their molecular target. Because MEK1 directly controls the phosphorylation and activation of ERK, blocking it can suppress abnormal MAPK signaling even when upstream oncogenic mutations are present. Indolinone derivatives, a class of compounds built around an indole-like ring system fused to a ketone, have long been recognized as privileged scaffolds in kinase drug discovery because their framework tolerates extensive chemical modification. Yet the precise structural features that determine how strongly these molecules inhibit MEK1 remained poorly understood, creating an opportunity for a systematic computational approach.

To bridge that knowledge gap, the team assembled a dataset of 33 hybrid 3-substituted 5-(phenylamino) indolone derivatives with experimentally measured anticancer activities, expressed as IC50 values and converted to their logarithmic counterpart, pIC50. They calculated a suite of molecular descriptors capturing physicochemical, topological, and electronic properties using Gaussian 03W and ChemBioOffice 3D software, then split the dataset randomly into 27 training compounds and 6 test compounds in an approximate 80:20 ratio. Using multiple linear regression, they distilled the model down to six descriptors: Bend, Stretch-Bend, 1,4 VDW, NHA (the number of hydrogen-bond acceptors), TC (total connectivity), and TVC (total valence connectivity). The final equation achieved a coefficient of determination of 0.741 with a root-mean-square error of just 0.144, indicating that a handful of geometric and connectivity features could explain much of the variation in biological activity across the series.

Rigorous validation separated this model from statistical noise. In a Y-randomization test, the researchers scrambled the activity values 500 times while keeping the descriptor matrix intact; the randomized models produced a mean R² of only 0.1871 and a mean leave-one-out cross-validation coefficient of −0.3801, both dramatically lower than the original model’s values of 0.8739 and 0.7736. Variance inflation factor analysis confirmed that multicollinearity among the six descriptors was not severe, with all values falling below the conventional threshold of 10. External validation against the six withheld test compounds met the criteria established by Golbraikh and Tropsha, with slopes and intercepts close to ideal. Together, these checks suggested the model captured genuine structure–activity relationships rather than chance correlations, though the authors caution that the relatively small dataset limits how far its predictions can be generalized.

Armed with the validated equation, the team designed ten new molecules, Pred 1 through Pred 10, by strategically modifying substituents on the indolinone scaffold while keeping descriptor values within the chemical space of the original dataset. Predicted pIC50 values ranged from 4.395 to 6.306, with Pred 9 (6.046) and Pred 10 (6.306) standing out as the most potent virtual candidates, exceeding even the most active compound in the original series. The researchers also proposed hypothetical synthetic routes for these compounds based on previously published procedures, although they emphasize that no actual synthesis has been attempted and the feasibility of the proposed pathways remains unproven.

To probe how the two lead candidates might engage MEK1 at the atomic level, the team turned to molecular docking against the MEK1 crystal structure deposited in the Protein Data Bank under accession code 3EQG, resolved at 2.50 angstroms with the clinical inhibitor mirdametinib (PD-0325901) bound in its active site. Before trusting the docking protocol, they validated it by extracting the co-crystallized ligand and re-docking it into the same site; the re-docked pose overlaid the experimental conformation with a root-mean-square deviation of just 0.90 angstroms, well below the accepted 2.0 angstrom threshold. Pred 9 then docked with a predicted binding energy of −10.27 kcal/mol, forming hydrogen bonds with Val-127 and Asp-190 at distances of 1.92 and 1.98 angstroms, along with pi-cation, pi-anion, pi-sigma, and pi-sulfur contacts involving Lys-97, Asp-208, Ile-216, Met-219, and Met-143. Pred 10 scored even better at −10.49 kcal/mol, weaving six hydrogen bonds with Val-211, Ser-212, Phe-209, Asp-208, Lys-97, and Ala-76.

Docking snapshots, however, capture only a frozen moment. To test whether the predicted complexes would hold together under realistic thermal motion, the researchers ran 100-nanosecond molecular dynamics simulations with the Desmond engine and the OPLS3e force field. The Pred 9–MEK1 complex settled into a stable regime after roughly 30 nanoseconds, with ligand RMSD fluctuating between 1.60 and 2.80 angstroms and averaging about 1.85 angstroms. The Pred 10 complex stabilized around 2.8 angstroms after the same equilibration period. Contact analysis revealed remarkably persistent interactions: Ile-126 held a hydrogen bond with Pred 9 for approximately 90 percent of the simulation, while Asp-190 maintained a water-mediated bridge throughout. For Pred 10, Asp-208 and Phe-209 each sustained hydrogen bonds for about 70 percent of the run. Radius of gyration and residue fluctuation profiles remained within normal ranges for both systems, suggesting the protein core stayed compact and stable while the ligands explored their binding pockets.

Beyond binding, a drug candidate must survive the journey through the body. In silico ADMET predictions using SwissADME and pkCSM painted an encouraging preliminary picture. Pred 9 and Pred 10 were predicted to achieve intestinal absorption above 82 percent, show moderate volumes of distribution, and—critically—be Ames-negative, meaning the models forecast no bacterial mutagenicity, in contrast to M31, the most active compound from the original dataset, which was predicted Ames-positive. Both new candidates also showed higher predicted aqueous solubility than M31. Molinspiration analysis confirmed that both molecules satisfy Lipinski’s Rule of Five with zero violations, with molecular weights of 428.52 and 444.52 g/mol and topological polar surface areas of 81.59 and 101.82 square angstroms respectively. POM analysis, combining Petra, Osiris, and Molinspiration tools, identified negatively charged carbonyl oxygens and positively charged NH hydrogens as potential pharmacophoric sites associated with antitumor activity. However, the analysis flagged predicted inhibition of the CYP1A2 and CYP3A4 metabolic enzymes as a potential liability, since blocking these enzymes could trigger drug–drug interactions in patients taking other medications.

The authors are careful to frame their results as hypothesis-generating rather than proof of efficacy. Pred 9 and Pred 10 exist only as computer models; they have never been synthesized, tested against MEK1 in enzymatic assays, or evaluated in cancer cells or animals. Every predicted activity, binding energy, pharmacokinetic parameter, and toxicity flag in the study is a computational estimate that must be confirmed experimentally. Still, the work exemplifies a modern drug discovery paradigm in which QSAR modeling, molecular docking, molecular dynamics, and ADMET prediction converge to triage thousands of possibilities down to a handful of synthesizable candidates before expensive laboratory work begins. The research team, led by Mohammed Er-rajy of Sidi Mohamed Ben Abdellah University in Fez alongside collaborators including Somdutt Mujwar of Chitkara University and Abdelouahid Samadi of United Arab Emirates University, plans to pursue chemical synthesis and in vivo testing of the two lead compounds. If those experiments bear fruit, the indolinone scaffold could join the arsenal of MEK1-targeted weapons being developed for lung and other cancers—provided the virtual promise survives contact with biological reality.

Subject of Research: Computational design of indolinone-based MEK1 inhibitor candidates for anticancer drug discovery

Article Title: In silico design of novel indolinone-based anticancer agents and proposal of synthetic routes

Article References: Er-rajy, M., El Fadili, M., Etabti, H., Aziz, A., Lenda, F., Mujwar, S., Chtita, S., Samadi, A., & Elhallaoui, M. (2026). In silico design of novel indolinone-based anticancer agents and proposal of synthetic routes. Frontiers in Bioinformatics, 6, Article 1964610. https://doi.org/10.3389/fbinf.2026.1964610

Image Credits: AI Generated

DOI: 10.3389/fbinf.2026.1964610

Keywords: indolinone, MEK1, QSAR, molecular docking, molecular dynamics, ADMET, RAF-MEK-ERK pathway, lung cancer, drug discovery, anticancer agents, computational chemistry, drug design