Molecules can be exposed to strong local electric fields of the order of 10⁸–10¹⁰ V m⁻¹ in the biological milieu. The effects of such fields on the rate constant (k) of a model reaction, the double-proton transfer reaction in the formic acid dimer (FAD), are investigated. The barrier heights and shapes are calculated in the absence and presence of several static homogenous external fields ranging from 5.14 × 10⁸ to 5.14 × 10⁹ V m⁻¹ using density functional theory (DFT/B3LYP) and second order Møller–Plesset perturbation theory (MP2) in conjunction with the 6-311++G(d,p) Pople basis set. Conventional transition state theory (CTST) followed by Wigner tunneling correction is then applied to estimate the rate constants at 25 °C. It is found that electric fields parallel to the long axis of the dimer (the line joining the two carbon atoms) lower the uncorrected barrier height, and hence increase the raw k. These fields also flatten the potential energy surface near the transition state region and, hence, decrease the multiplicative tunneling correction factor. The net result of these two opposing effects is that fields increase k(corrected) by a factor of ca. 3–4 (DFT–MP2, respectively) compared to the field-free k. Field strengths of ∼3 × 10⁹ V m⁻¹ are found to be sufficient to double the tunneling-corrected double proton transfer rate constant at 25 °C. Field strengths of similar orders of magnitudes are encountered in the scanning tunneling microscope (STM), in the microenvironment of a DNA base-pair, in an enzyme active site, and in intense laser radiation fields. It is shown that the net (tunneling corrected) effect of the field on k can be closely fitted to an exponential relationship of the form k = aexp(bE), where a and b are constants and E the electric field strength.

The use of electron density-based molecular descriptors in drug research, particularly in quantitative structure--activity relationships/quantitative structure--property relationships studies, is reviewed. The exposition starts by a discussion of molecular similarity and transferability in terms of the underlying electron density, which leads to a qualitative introduction to the quantum theory of atoms in molecules (QTAIM). The starting point of QTAIM is the topological analysis of the molecular electron-density distributions to extract atomic and bond properties that characterize every atom and bond in the molecule. These atomic and bond properties have considerable potential as bases for the construction of robust quantitative structure--activity/property relationships models as shown by selected examples in this review. QTAIM is applicable to the electron density calculated from quantum-chemical calculations and/or that obtained from ultra-high resolution x-ray diffraction experiments followed by nonspherical refinement. Atomic and bond properties are introduced followed by examples of application of each of these two families of descriptors. The review ends with a study whereby the molecular electrostatic potential, uniquely determined by the density, is used in conjunction with atomic properties to elucidate the reasons for the biological similarity of bioisosteres.

The discovery paradigm of drugs is rapidly growing due to advances in machine learning (ML) and artificial intelligence (AI). This review covers myriad faces of AI and ML in drug design. There is a... Graphical abstract In the middle, there is a human-shaped word cloud about drug design and artificial intelligence. The human shape represents the idea of personalized/precision medicine. The enzym...

Artificial intelligence (AI) has emerged as a transformative technology with significant potential to revolutionize disease understanding and drug design in healthcare. AI serves as a remarkable accelerating tool that bridges the gap between understanding diseases and discovering drugs. Given its capacity in the analysis and interpretation of massive amounts of data, AI is tremendously boosting the power of predictions with impressive accuracies. This allowed AI to pave the way for advancing all key stages of drug development, with the advantage of expediting the drug discovery process and curbing its costs. This is a comprehensive review of the recent advances in AI and its applications in drug discovery and development, starting with disease identification and spanning through the various stages involved in the drug discovery pipeline, including target identification, screening, lead discovery, and clinical trials. In addition, this review discusses the challenges that arise during the implementation of AI at each stage of the discovery process and provides insights into the future prospects of this field.

The relationship between base pair hydrogen bond proton transfer and the rate of spontaneous single point mutations at ambient temperatures and pressures in aqueous DNA is investigated. By using an ensemble-based multiscale computational modelling method, statistically robust rates of proton transfer for the A:T and G:C base pairs within a solvated DNA dodecamer are calculated. Several different proton transfer pathways are observed within the same base pair. It is shown that, in G:C, the double proton transfer tautomer is preferred, while the single proton transfer process is favoured in A:T. The reported range of rate coefficients for double proton transfer is consistent with recent experimental data. Notwithstanding the approximately 1000 times more common presence of single proton transfer products from A:T, observationally there is bias towards G:C to A:T mutations in a wide range of living organisms. We infer that the double proton transfer reactions between G:C base pairs have a negligible contribution towards this bias for the following reasons: (i) the maximum half-life of the G*:C* tautomer is in the range of picoseconds, which is significantly smaller than the milliseconds it takes for DNA to unwind during replication, (ii) statistically, the majority of G*:C* tautomers revert back to their canonical forms through a barrierless process, and (iii) the thermodynamic instability of the tautomers with respect to the canonical base pairs. Through similar reasoning, we also deduce that proton transfer in the A:T base pair does not contribute to single point mutations in DNA.