Prediction of Melting Temperature of Organic Molecules using Machine Learning

Accurate prediction of the melting point of oral drugs is crucial for understanding their chemical properties. Early identification of these properties aids in the screening of potential drugs, thereby saving resources in the pharmaceutical industry's discovery and manufacturing processes. The prediction of organic molecules is a complex task due to many factors that affect entropy and enthalpy forces within a molecule, which are dependent on various factors like shape, electronegativity, flexibility, rotatability, intermolecular bonding, etc. In this study, we curated a combined dataset of organic molecules, extracted from the Open Notebook Science Dataset and Cambridge Structure Database. The dataset consists of molecules composed of carbon, oxygen, nitrogen, sulfur, phosphorous, and halogens, exhibiting a wide range of melting point temperatures and molecules with complex structures. To gain insights into the significance of each feature and its contribution to melting point prediction, we divided the combined dataset into four subsets based on the number of bonds an atom can form. We perform feature engineering on these datasets by studying the physical and chemical properties known to impact melting points. Numerical features were derived from the molecules, capturing relevant information. Additionally, we utilized embedding features without any modifications. Machine learning models were trained using both numerical and embedding features, with the accuracy evaluated through R$^2$ scores and root mean squared error values. We set the model trained on embedding features as a benchmark for our model and features to surpass. Our machine learning models exhibited good performance, outperforming the benchmark and achieving good prediction accuracy. Furthermore, we conducted an in-depth analysis of the results to assess the impact of individual features on the models. We observed physical shape features and the presence of specific substructural groups exhibited a strong correlation with melting point prediction. To explore the relationship between features, we performed a principal component analysis. The findings of this study have important implications for drug development, formulation, and optimization of manufacturing processes. Accurate prediction of melting points enhances drug screening procedures and aids in the design of effective pharmaceutical products

A Channel Coding Perspective of Collaborative Filtering

We consider the problem of collaborative filtering from a channel coding perspective. We model the underlying rating matrix as a finite alphabet matrix with block constant structure. The observations are obtained from this underlying matrix through a discrete memoryless channel with a noisy part representing noisy user behavior and an erasure part representing missing data. Moreover, the clusters over which the underlying matrix is constant are {\it unknown}. We establish a sharp threshold result for this model: if the largest cluster size is smaller than $C_1 \log(mn)$ (where the rating matrix is of size $m \times n$), then the underlying matrix cannot be recovered with any estimator, but if the smallest cluster size is larger than $C_2 \log(mn)$, then we show a polynomial time estimator with diminishing probability of error. In the case of uniform cluster size, not only the order of the threshold, but also the constant is identified.Comment: 32 pages, 1 figure, Submitted to IEEE Transactions on Information Theor

Andrographolide: A Novel Antimalarial Diterpene Lactone Compound from Andrographis paniculata and Its Interaction with Curcumin and Artesunate

Andrographolide (AND), the diterpene lactone compound, was purified by HPLC from the methanolic fraction of the plant Andrographis paniculata. The compound was found to have potent antiplasmodial activity when tested in isolation and in combination with curcumin and artesunate against the erythrocytic stages of Plasmodium falciparum in vitro and Plasmodium berghei ANKA in vivo. IC50s for artesunate (AS), andrographolide (AND), and curcumin (CUR) were found to be 0.05, 9.1 and 17.4 μM, respectively. The compound (AND) was found synergistic with curcumin (CUR) and addictively interactive with artesunate (AS). In vivo, andrographolide-curcumin exhibited better antimalarial activity, not only by reducing parasitemia (29%), compared to the control (81%), but also by extending the life span by 2-3 folds. Being nontoxic to the in vivo system this agent can be used as template molecule for designing new derivatives with improved antimalarial properties

Anti-malarial activities of Andrographis paniculata and Hedyotis corymbosa extracts and their combination with curcumin

<p>Abstract</p> <p>Background</p> <p>Herbal extracts of <it>Andrographis paniculata </it>(AP) and <it>Hedyotis corymbosa </it>(HC) are known as hepato-protective and fever-reducing drugs since ancient time and they have been used regularly by the people in the south Asian sub-continent. Methanolic extracts of these two plants were tested in vitro on choloroquine sensitive (MRC-pf-20) and resistant (MRC-pf-303) strains of <it>Plasmodium falciparum </it>for their anti-malarial activity.</p> <p>Methods</p> <p>Growth inhibition was determined using different concentrations of these plant extracts on synchronized <it>P. falciparum </it>cultures at the ring stage. The interactions between these two plant extracts and individually with curcumin were studied in vitro. The performance of these two herbal extracts in isolation and combination were further evaluated in vivo on Balb/c mice infected with <it>Plasmodium berghei </it>ANKA and their efficacy was compared with that of curcumin. The in vivo toxicity of the plant derived compounds as well as their parasite stage-specificity was studied.</p> <p>Results</p> <p>The 50% inhibitory concentration (IC₅₀) of AP (7.2 μg/ml) was found better than HC (10.8 μg/ml). Combination of these two herbal drugs showed substantial enhancement in their anti-malarial activity. Combinatorial effect of each of these with curcumin also revealed anti-malarial effect. Additive interaction between the plant extracts (AP + HC) and their individual synergism with curcumin (AP+CUR, HC+CUR) were evident from this study. Increased in vivo potency was also observed with the combination of plant extracts over the individual extracts and curcumin. Both the plant extracts were found to inhibit the ring stage of the parasite and did not show any in vivo toxicity, whether used in isolation or in combination.</p> <p>Conclusion</p> <p>Both these two plant extracts in combination with curcumin could be an effective, alternative source of herbal anti-malarial drugs.</p

Empowering Driver-Passenger Collaboration: Designing In-Car Systems with a focus on Social Connectedness, Fairness, and Team Performance

Driving a car can be difficult when it comes to distractions caused by operating the in-vehicle infotainment system (IVIS). In-car passengers often help with performing IVIS-related tasks. However, an IVIS is often not designed with a focus on task collaboration. In this article, we focus on how to design in-car systems with the goal to support collaboration between a driver and a front-seat passenger. Based on infotainment-oriented tasks, we initially explore five key collaborative control concepts by means of an IVIS which differ from each other in terms of the number of available IVIS screens (one or two), access to menus (restricted and unrestricted), and the nature of performing tasks in parallel or one after the other. Results from a simulator study with N = 16 pairs show significant effects of the concepts on social collaboration in terms of perceived social connectedness (measured with sub-dimensions connectedness, affiliation, belongingness, companionship), team performance (coordination effectiveness and team cohesion), and fairness. We found that especially a dedicated passenger IVIS screen empowers front-seat passengers, reduces power dynamics, supports fairness, and minimizes driver distraction (caused by interacting passengers). We discuss the implications of these findings and posit recommendations to design future IVIS in passenger cars with improved driver-passenger collaboration by explicitly designing for balanced power roles, situational awareness, active communication, and a balance between drivers’ privacy and trust toward the passenger. Additionally, we outline a systematic overview of future work to explore the research field of driver-passenger collaboration in more breadth and depth.</p

Effect of event classifiers on jet quenching-like signatures in high-multiplicity p+pp+p collisions at s=13\sqrt{s} = 13 TeV

The motivation behind exploring jet quenching-like phenomena in small systems arises from the experimental observation of heavy-ion-like behavior of particle production in high-multiplicity proton-proton ($p+p$) collisions. Quantifying the jet quenching in $p+p$ collisions is a challenging task, as the magnitude of the nuclear modification factor ($R_{\rm AA}$ or $R_{\rm CP}$), which is used to quantify jet quenching, is influenced by several factors, such as the estimation of centrality and the scaling factor. The most common method of centrality estimation employed by the ALICE collaboration is based on measuring charged-particle multiplicity with the V0 detector situated at the forward rapidity. This technique of centrality estimation makes the event sample biased towards hard processes like multijet final states. This bias of the V0 detector towards hard processes makes it difficult to study the jet quenching effect in high-multiplicity $p+p$ collisions. In the present article, we propose to explore the use of a new and robust event classifier, flattenicity which is sensitive to both the multiple soft partonic interactions and hard processes. The $\mathcal{P}_{\rm CP}$, a quantity analogous to $R_{\rm CP}$, has been estimated for high-multiplicity $p+p$ collisions at $\sqrt{s} = 13$ TeV using \texttt{PYTHIA8} model for both the V0M (the multiplicity classes selected based on V0 detector acceptance) as well as flattenicity. The evolution of $\mathcal{P}_{\rm CP}$ with $p_{\rm T}$ shows a heavy-ion-like effect for flattencity which is attributed to the selection of softer transverse momentum particles in high-multiplicity $p+p$ collisions.Comment: 7 pages, 6 figure

Thermophysical, excess and transport properties of organic solvents with imidazolium based ionic liquids

Ultrasonic velocity and refractive index have been evaluated for eight binary mixtures comprising imidazolium based ([BMIM][PF6], [HMIM][PF6], [OMIM][PF6] and [MMIM][CH3SO4]) ionic liquids with three organic solvents of varying nature, viz., 2-propanol, 2-butanone and ethylacetate, at three different temperatures (293.15, 298.15 and 303.15 K). Evaluation of refractive index has been carried out by eight approaches, whereas five methods have been employed for computation of ultrasonic velocity. Molecular interaction studies have been carried out with the help of intermolecular free length, and interaction parameter. Furthermore, the excess counterpart of the coefficient of thermal expansion has been determined to get a deeper understanding on the behavior in terms of nature and extent of interactions present in these systems