Chemoinformatics and bioinformatics are emerging scientific disciplines. While chemoinformatics deals with analysis of small-molecules, bioinformatics is more concerned with large macromolecular sequence and structural information (DNA, RNA or Protein). Although there are fundamental differences between bioinformatics and chemoinformatics, both utilized variety of similar computational techniques. Machine learning and data mining approaches such as Markov models, neural networks, principal component analysis are equally relevant in bioinformatics as well as in chemoinformatics. In chemoinformatics concept of molecular descriptors is extensively exploited to characterize structural features, activity and properties of small molecules. Learning from theses molecular descriptors one can develop new methods to solve bioinformatics problems. For example, problem of protein classification by shape-structure using 3D structural information can be resolved using Shadow indices. Shadow indices represent a set of 10 geometric descriptors , where descriptors are calculated by projecting the molecular surface on three mutually perpendicular planes, XY, YZ, and XZ. Similarly one can apply concept of binary fingerprints from chemoinformatics to analyze the protein sequence information in bioinformatics. We ourselves developed a novel method of scoring residue conservation using property fingerprints (manuscript under review). Our approach converts a protein sequence into a one-dimensional binary string (or property fingerprint) using different amino acid properties, subsequently similarity and dissimilarity measures such as tonimoto coefficient are calculated to score the residue conservation. This kind of learning is not unidirectional, chemoinformatics as well can learn a lot from bioinformatics. Take a example of phylogenetic trees, they can reveal relationships among various biological species or other entities believed to have a common ancestor. Similar kind of formulations can be used to represent the chemical space explored by nature for natural products exposing the underlying principal of evolutionary selection. Scope of these acquisitions is not restricted to development of algorithms and methods, similar kind of mutual learning can guide development of better web applications & tools, and it would be interesting to see how we can bring best out of this.