Automated and enabling technologies for medicinal chemistry

Auto-RapTAC: A Versatile and Sustainable Platform for the Automated Rapid Synthesis and Evaluation of PROTAC

The tedious synthesis and limited throughput biological evaluation remain a great challenge for discovering new proteolysis targeting chimera (PROTAC). To rapidly identify potential PROTAC lead compounds, we report a platform named Auto-RapTAC. Based on the modular characteristic of the PROTAC molecule, a streamlined workflow that integrates lab automation with "click chemistry" joint building-block libraries was constructed. This facilitates the autonomous generation of a variety of PROTACs, each with distinct linkers and E3 ligase ligands, all stored in biocompatible solutions. The ready-for-screening (R4S) approach, when paired with fluorescence-based assays, enables the efficient assessment of the PROTAC degradation activity in a high-throughput manner. To further test the capability of the platform, we identify six new PROTACs that target CDK2, CDK12, and BCL6 within a mere 8-day time frame for each target. In all, this platform could find broad application not only in discovering new PROTACs but also in the rapid development of novel heterobifunctional modalities.

Synthesis, spectroscopic analysis, and computational-based investigations on 'azo-coumarin-Co(II)-galangin' hybrids exhibit multipotential activities

The present study synthesized a series of cobalt (II) metal ion frame hybrid candidates (6a-6f) bearing phyto-flavonol galangin with substituted aryl diazenyl coumarins, and further structural confirmation was validated by various spectral techniques, including NMR, ATR-FTIR, UV-vis, HPLC, XRD, etc. Therapeutic potency was investigated via PASS (prediction of activity spectra for substances), molecular docking, molecular dynamics simulation, prediction of toxicity, pharmacokinetics, and drug-likeness scores, along with the highest occupied molecular orbital (HOMO), the lowest unoccupied molecular orbital (LUMO), with their energy gaps (ΔEH-L) to locate the most potential therapeutic candidates. The PASS prediction (Pa > Pi score) showed that proposed metal complexes have kinase inhibitors, antioxidative, and antischistosomal activities with potential molecular docking scores (> -7 kcal/mol) against selected targeted enzymes. Further, the MD-simulation (RMSD, RMSF, Rg, and H-bonds) of the most potential docking complex, 'HER2-6d', showed a minimum deviation similar to the standard drug (lapatinib) at 100 ns, indicating that 6d could be a potential noncovalent anticancer inhibitor. In addition, metal complexes possess a non-toxic and ideal drug-ability profiles, and positive electron space in an excited state increases the binding affinity towards target enzymes. Among all six ligands, 6c and 6d were the two most multipotent therapeutic agents from the above analyses. In summary, this could be a feasible approach towards the utilization of phytochemicals in mainstream therapeutic applications, where bioinformatics tools help to select a lead drug candidate at an early stage and guide for higher experimental success by proceeding with potential candidates.

The case for data science in experimental chemistry: examples and recommendations

The physical sciences community is increasingly taking advantage of the possibilities offered by modern data science to solve problems in experimental chemistry and potentially to change the way we design, conduct and understand results from experiments. Successfully exploiting these opportunities involves considerable challenges. In this Expert Recommendation, we focus on experimental co-design and its importance to experimental chemistry. We provide examples of how data science is changing the way we conduct experiments, and we outline opportunities for further integration of data science and experimental chemistry to advance these fields. Our recommendations include establishing stronger links between chemists and data scientists; developing chemistry-specific data science methods; integrating algorithms, software and hardware to 'co-design' chemistry experiments from inception; and combining diverse and disparate data sources into a data network for chemistry research.