Mixed-stack charge transfer crystals of pillar[5]quinone and tetrathiafulvalene exhibiting ferroelectric features - Shivakumar, Kilingaru I.; Swathi, Kadaba; Goudappagouda; Das, Tamal C.; Kumar, Ashwani; Makde, Ravindra D.; Vanka, Kumar; Narayan, Kavassery S.; Babu, Sukumaran Santhosh; Sanjayan, Gangadhar J. (Chemistry- A European Journal, 2017)
Ferroelectric materials find extensive applications in the fabrication of compact memory devices and ultra-sensitive multifunctional detectors. Face-to-face alternate stacking of electron donors and acceptors effectuate long-range unidirectional ordering of charge-transfer (CT) dipoles, promising tunable ferroelectricity. Herein we report a new TTF-quinone system-an emerald green CT complex consisting pillar[5]quinone (P5Q) and tetrathiafulvalene (TTF). The CT crystals, as determined by single crystal synchrotron X-ray diffraction, adopt a 1:1 mixed-stack arrangement of donor and acceptor with alternating dimers of TTF and 1,4-dioxane encapsulated P5Q. The TTF-P5Q.dioxane crystal possesses a macroscopic polarization axis giving rise to ferroelectricity at room temperature. The CT complex manifests ferroelectric features such as optical polarization rotation, temperature-dependent phase transition and piezoelectric response in single crystals. Ferroelectric behavior observed in P5Q-based CT complex widens the scope for further work on this structurally intriguing and readily accessible cyclic pentaquinone.
Keywords: Acceptor; Charge-transfer complexes; Chemistry; Conductors; DDQ; Donor; ferroelectric; Macrocycles; Organic Metals; Pi-interaction; Pillar[5]quinone; salts; Transfer Complexes; Transport

Machine learning classifiers aid virtual screening for efficient design of mini-protein therapeutics - Gaur, Neeraj K.; Goyal, Venuka Durani; Kulkarni, Kiran; Makde, Ravindra D. (Bioorganic & Medicinal Chemistry Letters, 2021)
De novo design of mini-proteins (4-12 kDa) has recently been shown to produce new candidates for protein therapeutics. They are temperature stable molecules that bind to the drug target with high affinity for inhibiting its interactions. The development of mini-protein binders requires laboratory screening of tens of thousands of molecules for effective target binding. In this study we trained machine learning classifiers which can distinguish, with 90% accuracy and 80% precision, mini-protein binders from non-binding molecules designed for a particular target; this significantly reduces the number of mini protein candidates for experimental screening. Further, on the basis of our results we propose a multi-stage protocol where a small dataset (few hundred experimentally verified target-specific mini-proteins) can be used to train classifiers for improving the efficiency of mini-protein design for any specific target.
Keywords: Drug design; machine learning; Mini-proteins; Protein therapeutics; virtual screening